1
|
Lee HT, Chiu PY, Yen CW, Chou ST, Tseng YC. Application of artificial intelligence in lateral cephalometric analysis. J Dent Sci 2024; 19:1157-1164. [PMID: 38618076 PMCID: PMC11010784 DOI: 10.1016/j.jds.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 04/16/2024] Open
Affiliation(s)
- Huang-Ting Lee
- School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Po-Yuan Chiu
- School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
- Department of Orthodontics, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Chen-Wen Yen
- Department of Mechanical and Electromechanical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Szu-Ting Chou
- School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
- Department of Orthodontics, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Yu-Chuan Tseng
- School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
- Department of Orthodontics, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| |
Collapse
|
2
|
S R, S S, S Murthy P, Deshmukh S. Landmark annotation through feature combinations: a comparative study on cephalometric images with in-depth analysis of model's explainability. Dentomaxillofac Radiol 2024; 53:115-126. [PMID: 38166356 DOI: 10.1093/dmfr/twad011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/16/2023] [Accepted: 11/16/2023] [Indexed: 01/04/2024] Open
Abstract
OBJECTIVES The objectives of this study are to explore and evaluate the automation of anatomical landmark localization in cephalometric images using machine learning techniques, with a focus on feature extraction and combinations, contextual analysis, and model interpretability through Shapley Additive exPlanations (SHAP) values. METHODS We conducted extensive experimentation on a private dataset of 300 lateral cephalograms to thoroughly study the annotation results obtained using pixel feature descriptors including raw pixel, gradient magnitude, gradient direction, and histogram-oriented gradient (HOG) values. The study includes evaluation and comparison of these feature descriptions calculated at different contexts namely local, pyramid, and global. The feature descriptor obtained using individual combinations is used to discern between landmark and nonlandmark pixels using classification method. Additionally, this study addresses the opacity of LGBM ensemble tree models across landmarks, introducing SHAP values to enhance interpretability. RESULTS The performance of feature combinations was assessed using metrics like mean radial error, standard deviation, success detection rate (SDR) (2 mm), and test time. Remarkably, among all the combinations explored, both the HOG and gradient direction operations demonstrated significant performance across all context combinations. At the contextual level, the global texture outperformed the others, although it came with the trade-off of increased test time. The HOG in the local context emerged as the top performer with an SDR of 75.84% compared to others. CONCLUSIONS The presented analysis enhances the understanding of the significance of different features and their combinations in the realm of landmark annotation but also paves the way for further exploration of landmark-specific feature combination methods, facilitated by explainability.
Collapse
Affiliation(s)
- Rashmi S
- Dept. of Computer Science and Engineering, Sri Jayachamarajendra College of Engineering, JSS Science and Technology University, Mysuru, 570006, India
| | - Srinath S
- Dept. of Computer Science and Engineering, Sri Jayachamarajendra College of Engineering, JSS Science and Technology University, Mysuru, 570006, India
| | - Prashanth S Murthy
- Dept. of Pediatric & Preventive Dentistry, JSS Dental College & Hospital, JSS Academy of Higher Education & Research, Mysuru, 570015, India
| | - Seema Deshmukh
- Dept. of Pediatric & Preventive Dentistry, JSS Dental College & Hospital, JSS Academy of Higher Education & Research, Mysuru, 570015, India
| |
Collapse
|
3
|
Kazimierczak N, Kazimierczak W, Serafin Z, Nowicki P, Nożewski J, Janiszewska-Olszowska J. AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review. J Clin Med 2024; 13:344. [PMID: 38256478 PMCID: PMC10816993 DOI: 10.3390/jcm13020344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 12/29/2023] [Accepted: 01/05/2024] [Indexed: 01/24/2024] Open
Abstract
The advent of artificial intelligence (AI) in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. Its usage in orthodontic practices worldwide has increased with the availability of various AI applications and tools. This review explores the principles of AI, its applications in orthodontics, and its implementation in clinical practice. A comprehensive literature review was conducted, focusing on AI applications in dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision making, and patient telemonitoring. Due to study heterogeneity, no meta-analysis was possible. AI has demonstrated high efficacy in all these areas, but variations in performance and the need for manual supervision suggest caution in clinical settings. The complexity and unpredictability of AI algorithms call for cautious implementation and regular manual validation. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for successful integration into orthodontic practice.
Collapse
Affiliation(s)
- Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Wojciech Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Paweł Nowicki
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Jakub Nożewski
- Department of Emeregncy Medicine, University Hospital No 2 in Bydgoszcz, Ujejskiego 75, 85-168 Bydgoszcz, Poland
| | | |
Collapse
|
4
|
Hartoonian S, Hosseini M, Yousefi I, Mahdian M, Ghazizadeh Ahsaie M. Applications of artificial intelligence in dentomaxillofacial imaging-a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol 2024:S2212-4403(23)01566-3. [PMID: 38637235 DOI: 10.1016/j.oooo.2023.12.790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/02/2023] [Accepted: 12/22/2023] [Indexed: 04/20/2024]
Abstract
BACKGROUND Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities. STUDY DESIGN A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another. RESULTS The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection. CONCLUSION Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.
Collapse
Affiliation(s)
- Serlie Hartoonian
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Matine Hosseini
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Iman Yousefi
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mina Mahdian
- Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Mitra Ghazizadeh Ahsaie
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| |
Collapse
|
5
|
Takahashi K, Shimamura Y, Tachiki C, Nishii Y, Hagiwara M. Cephalometric landmark detection without X-rays combining coordinate regression and heatmap regression. Sci Rep 2023; 13:20011. [PMID: 37974018 PMCID: PMC10654665 DOI: 10.1038/s41598-023-46919-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 11/07/2023] [Indexed: 11/19/2023] Open
Abstract
Fully automated techniques using convolutional neural networks for cephalometric landmark detection have recently advanced. However, all existing studies have adopted X-rays. The problem of direct exposure of patients to X-ray radiation remains unsolved. We propose a model for detecting cephalometric landmarks using only facial profile images without X-rays. First, the model estimates the landmark coordinates using the features of facial profile images through high-resolution representation learning. Second, considering the spatial relationship of the landmarks, the model refines the estimated coordinates. The estimated coordinates are input into fully connected networks to improve the accuracy. During the experiment, a total of 2000 facial profile images collected from 2000 female patients were used. Experiments results suggested that the proposed method may perform at a level equal to or potentially better than existing methods using cephalograms. We obtained an MRE of 0.61 mm for the test data and a mean detection rate of 98.20% within 2 mm. Our proposed two-stage learning method enables a highly accurate estimation of the landmark positions using only facial profile images. The results indicate that X-rays may not be required when detecting cephalometric landmarks.
Collapse
Affiliation(s)
- Kaisei Takahashi
- Department of Information and Computer Science, Faculty of Science and Technology, Keio University, Kanagawa, 223-8522, Japan.
| | - Yui Shimamura
- Department of Orthodontics, Tokyo Dental College, Tokyo, 101-0061, Japan
| | - Chie Tachiki
- Department of Orthodontics, Tokyo Dental College, Tokyo, 101-0061, Japan
| | - Yasushi Nishii
- Department of Orthodontics, Tokyo Dental College, Tokyo, 101-0061, Japan
| | - Masafumi Hagiwara
- Department of Information and Computer Science, Faculty of Science and Technology, Keio University, Kanagawa, 223-8522, Japan
| |
Collapse
|
6
|
Miragall MF, Knoedler S, Kauke-Navarro M, Saadoun R, Grabenhorst A, Grill FD, Ritschl LM, Fichter AM, Safi AF, Knoedler L. Face the Future-Artificial Intelligence in Oral and Maxillofacial Surgery. J Clin Med 2023; 12:6843. [PMID: 37959310 PMCID: PMC10649053 DOI: 10.3390/jcm12216843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 10/24/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023] Open
Abstract
Artificial intelligence (AI) has emerged as a versatile health-technology tool revolutionizing medical services through the implementation of predictive, preventative, individualized, and participatory approaches. AI encompasses different computational concepts such as machine learning, deep learning techniques, and neural networks. AI also presents a broad platform for improving preoperative planning, intraoperative workflow, and postoperative patient outcomes in the field of oral and maxillofacial surgery (OMFS). The purpose of this review is to present a comprehensive summary of the existing scientific knowledge. The authors thoroughly reviewed English-language PubMed/MEDLINE and Embase papers from their establishment to 1 December 2022. The search terms were (1) "OMFS" OR "oral and maxillofacial" OR "oral and maxillofacial surgery" OR "oral surgery" AND (2) "AI" OR "artificial intelligence". The search format was tailored to each database's syntax. To find pertinent material, each retrieved article and systematic review's reference list was thoroughly examined. According to the literature, AI is already being used in certain areas of OMFS, such as radiographic image quality improvement, diagnosis of cysts and tumors, and localization of cephalometric landmarks. Through additional research, it may be possible to provide practitioners in numerous disciplines with additional assistance to enhance preoperative planning, intraoperative screening, and postoperative monitoring. Overall, AI carries promising potential to advance the field of OMFS and generate novel solution possibilities for persisting clinical challenges. Herein, this review provides a comprehensive summary of AI in OMFS and sheds light on future research efforts. Further, the advanced analysis of complex medical imaging data can support surgeons in preoperative assessments, virtual surgical simulations, and individualized treatment strategies. AI also assists surgeons during intraoperative decision-making by offering immediate feedback and guidance to enhance surgical accuracy and reduce complication rates, for instance by predicting the risk of bleeding.
Collapse
Affiliation(s)
- Maximilian F. Miragall
- Department of Oral and Maxillofacial Surgery, University Hospital Regensburg, 93053 Regensburg, Germany
- Department of Oral and Maxillofacial Surgery, School of Medicine, Technical University of Munich, 81675 Munich, Germany
| | - Samuel Knoedler
- Division of Plastic Surgery, Department of Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT 06510, USA
| | - Martin Kauke-Navarro
- Division of Plastic Surgery, Department of Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT 06510, USA
| | - Rakan Saadoun
- Department of Plastic Surgery, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Alex Grabenhorst
- Department of Oral and Maxillofacial Surgery, School of Medicine, Technical University of Munich, 81675 Munich, Germany
| | - Florian D. Grill
- Department of Oral and Maxillofacial Surgery, School of Medicine, Technical University of Munich, 81675 Munich, Germany
| | - Lucas M. Ritschl
- Department of Oral and Maxillofacial Surgery, School of Medicine, Technical University of Munich, 81675 Munich, Germany
| | - Andreas M. Fichter
- Department of Oral and Maxillofacial Surgery, School of Medicine, Technical University of Munich, 81675 Munich, Germany
| | - Ali-Farid Safi
- Craniologicum, Center for Cranio-Maxillo-Facial Surgery, 3011 Bern, Switzerland;
- Faculty of Medicine, University of Bern, 3010 Bern, Switzerland
| | - Leonard Knoedler
- Division of Plastic Surgery, Department of Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Plastic, Hand and Reconstructive Surgery, University Hospital Regensburg, 93053 Regensburg, Germany
| |
Collapse
|
7
|
Rauniyar S, Jena S, Sahoo N, Mohanty P, Dash BP. Artificial Intelligence and Machine Learning for Automated Cephalometric Landmark Identification: A Meta-Analysis Previewed by a Systematic Review. Cureus 2023; 15:e40934. [PMID: 37496553 PMCID: PMC10368300 DOI: 10.7759/cureus.40934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/24/2023] [Indexed: 07/28/2023] Open
Abstract
Digital dentistry has become an integral part of our practice today, with artificial intelligence (AI) playing the predominant role. The present systematic review was intended to detect the accuracy of landmarks identified cephalometrically using machine learning and artificial intelligence and compare the same with the manual tracing (MT) group. According to the PRISMA-DTA guidelines, a scoping evaluation of the articles was performed. Electronic databases like Doaj, PubMed, Scopus, Google Scholar, and Embase from January 2001 to November 2022 were searched. Inclusion and exclusion criteria were applied, and 13 articles were studied in detail. Six full-text articles were further excluded (three articles did not provide a comparison between manual tracing and AI for cephalometric landmark detection, and three full-text articles were systematic reviews and meta-analyses). Finally, seven articles were found appropriate to be included in this review. The outcome of this systematic review has led to the conclusion that AI, when employed for cephalometric landmark detection, has shown extremely positive and promising results as compared to manual tracing.
Collapse
Affiliation(s)
- Sabita Rauniyar
- Orthodontics and Dentofacial Orthopaedics, Kalinga Institute of Dental Science, Bhubaneswar, IND
| | - Sanghamitra Jena
- Department of Orthodontics and Dentofacial Orthopaedics, Kalinga Institute of Dental Sciences, Kalinga Institute of Industrial Technology (KIIT) (Deemed to be University), Bhubaneswar, IND
| | - Nivedita Sahoo
- Department of Orthodontics and Dentofacial Orthopaedics, Kalinga Institute of Dental Sciences, Kalinga Institute of Industrial Technology (KIIT) (Deemed to be University), Bhubaneswar, IND
| | - Pritam Mohanty
- Department of Orthodontics, Kalinga Institute of Dental Sciences, Odisha, IND
| | - Bhagabati P Dash
- Department of Orthodontics and Dentofacial Orthopaedics, Kalinga Institute of Dental Sciences, Kalinga Institute of Industrial Technology (KIIT) (Deemed to be University), Bhubaneswar, IND
| |
Collapse
|
8
|
Zhao C, Yuan Z, Luo S, Wang W, Ren Z, Yao X, Wu T. Automatic recognition of cephalometric landmarks via multi-scale sampling strategy. Heliyon 2023; 9:e17459. [PMID: 37416642 PMCID: PMC10320076 DOI: 10.1016/j.heliyon.2023.e17459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 06/08/2023] [Accepted: 06/19/2023] [Indexed: 07/08/2023] Open
Abstract
The identification of head landmarks in cephalometric analysis significantly contributes in the anatomical localization of maxillofacial tissues for orthodontic and orthognathic surgery. However, the existing methods face the limitations of low accuracy and cumbersome identification process. In this pursuit, the present study proposed an automatic target recognition algorithm called Multi-Scale YOLOV3 (MS-YOLOV3) for the detection of cephalometric landmarks. It was characterized by multi-scale sampling strategies for shallow and deep features at varied resolutions, and especially contained the module of spatial pyramid pooling (SPP) for highest resolution. The proposed method was quantitatively and qualitatively compared with the classical YOLOV3 algorithm on the two data sets of public lateral cephalograms, undisclosed anterior-posterior (AP) cephalograms, respectively, for evaluating the performance. The proposed MS-YOLOV3 algorithm showed better robustness with successful detection rates (SDR) of 80.84% within 2 mm, 93.75% within 3 mm, and 98.14% within 4 mm for lateral cephalograms, and 85.75% within 2 mm, 92.87% within 3 mm, and 96.66% within 4 mm for AP cephalograms, respectively. It was concluded that the proposed model could be robustly used to label the cephalometric landmarks on both lateral and AP cephalograms for the clinical application in orthodontic and orthognathic surgery.
Collapse
Affiliation(s)
- Congyi Zhao
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Zengbei Yuan
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Shichang Luo
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Wenjie Wang
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Zhe Ren
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Xufeng Yao
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
| | - Tao Wu
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
| |
Collapse
|
9
|
Ao Y, Wu H. Feature Aggregation and Refinement Network for 2D Anatomical Landmark Detection. J Digit Imaging 2023; 36:547-561. [PMID: 36401132 PMCID: PMC10039137 DOI: 10.1007/s10278-022-00718-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 11/19/2022] Open
Abstract
Localization of anatomical landmarks is essential for clinical diagnosis, treatment planning, and research. This paper proposes a novel deep network named feature aggregation and refinement network (FARNet) for automatically detecting anatomical landmarks. FARNet employs an encoder-decoder structure architecture. To alleviate the problem of limited training data in the medical domain, we adopt a backbone network pre-trained on natural images as the encoder. The decoder includes a multi-scale feature aggregation module for multi-scale feature fusion and a feature refinement module for high-resolution heatmap regression. Coarse-to-fine supervisions are applied to the two modules to facilitate end-to-end training. We further propose a novel loss function named Exponential Weighted Center loss for accurate heatmap regression, which focuses on the losses from the pixels near landmarks and suppresses the ones from far away. We evaluate FARNet on three publicly available anatomical landmark detection datasets, including cephalometric, hand, and spine radiographs. Our network achieves state-of-the-art performances on all three datasets. Code is available at https://github.com/JuvenileInWind/FARNet .
Collapse
Affiliation(s)
- Yueyuan Ao
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731 China
| | - Hong Wu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731 China
| |
Collapse
|
10
|
Suhail S, Harris K, Sinha G, Schmidt M, Durgekar S, Mehta S, Upadhyay M. Learning Cephalometric Landmarks for Diagnostic Features Using Regression Trees. Bioengineering (Basel) 2022; 9:bioengineering9110617. [PMID: 36354530 PMCID: PMC9687964 DOI: 10.3390/bioengineering9110617] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/14/2022] [Accepted: 10/22/2022] [Indexed: 11/30/2022] Open
Abstract
Lateral cephalograms provide important information regarding dental, skeletal, and soft-tissue parameters that are critical for orthodontic diagnosis and treatment planning. Several machine learning methods have previously been used for the automated localization of diagnostically relevant landmarks on lateral cephalograms. In this study, we applied an ensemble of regression trees to solve this problem. We found that despite the limited size of manually labeled images, we can improve the performance of landmark detection by augmenting the training set using a battery of simple image transforms. We further demonstrated the calculation of second-order features encoding the relative locations of landmarks, which are diagnostically more important than individual landmarks.
Collapse
Affiliation(s)
- Sameera Suhail
- Department of Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
| | | | - Gaurav Sinha
- Departments of Computer Science & Statistics, University of British Columbia (Alumni), Vancouver, BC V6T1Z4, Canada
| | - Maayan Schmidt
- School of Dental Medicine, University of Connecticut Health, Farmington, CT 06030, USA
| | - Sujala Durgekar
- Department of Orthodontics, KLES’ Institute of Dental Sciences, Bangalore 560022, India
| | - Shivam Mehta
- Department of Developmental Sciences/Orthodontics, Marquette University, Milwaukee, WI 53202, USA
| | - Madhur Upadhyay
- Division of Orthodontics, University of Connecticut Health, Farmington, CT 06030, USA
- Correspondence:
| |
Collapse
|
11
|
Cephalometric Analysis in Orthodontics Using Artificial Intelligence-A Comprehensive Review. BIOMED RESEARCH INTERNATIONAL 2022; 2022:1880113. [PMID: 35757486 PMCID: PMC9225851 DOI: 10.1155/2022/1880113] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 05/13/2022] [Indexed: 11/17/2022]
Abstract
Artificial intelligence (AI) is a branch of science concerned with developing programs and computers that can gather data, reason about it, and then translate it into intelligent actions. AI is a broad area that includes reasoning, typical linguistic dispensation, machine learning, and planning. In the area of medicine and dentistry, machine learning is currently the most widely used AI application. This narrative review is aimed at giving an outline of cephalometric analysis in orthodontics using AI. Latest algorithms are developing rapidly, and computational resources are increasing, resulting in increased efficiency, accuracy, and reliability. Current techniques for completely automatic identification of cephalometric landmarks have considerably improved efficiency and growth prospects for their regular use. The primary considerations for effective orthodontic treatment are an accurate diagnosis, exceptional treatment planning, and good prognosis estimation. The main objective of the AI technique is to make dentists' work more precise and accurate. AI is increasingly being used in the area of orthodontic treatment. It has been evidenced to be a time-saving and reliable tool in many ways. AI is a promising tool for facilitating cephalometric tracing in routine clinical practice and analyzing large databases for research purposes.
Collapse
|
12
|
Lu G, Zhang Y, Kong Y, Zhang C, Coatrieux JL, Shu H. Landmark Localization for Cephalometric Analysis using Multiscale Image Patch-based Graph Convolutional Networks. IEEE J Biomed Health Inform 2022; 26:3015-3024. [PMID: 35259123 DOI: 10.1109/jbhi.2022.3157722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Accurate and robust cephalometric image analysis plays an essential role in orthodontic diagnosis, treatment assessment and surgical planning. This paper proposes a novel landmark localization method for cephalometric analysis using multiscale image patch-based graph convolutional networks. In detail, image patches with the same size are hierarchically sampled from the Gaussian pyramid to well preserve multiscale context information. We combine local appearance and shape information into spatialized features with an attention module to enrich node representations in graph. The spatial relationships of landmarks are built with the incorporation of three-layer graph convolutional networks, and multiple landmarks are simultaneously updated and moved toward the targets in a cascaded coarse-to-fine process. Quantitative results obtained on publicly available cephalometric X-ray images have exhibited superior performance compared with other state-of-the-art methods in terms of mean radial error and successful detection rate within various precision ranges. Our approach performs significantly better especially in the clinically accepted range of 2 mm and this makes it suitable in cephalometric analysis and orthognathic surgery.
Collapse
|
13
|
Putra RH, Doi C, Yoda N, Astuti ER, Sasaki K. Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofac Radiol 2022; 51:20210197. [PMID: 34233515 PMCID: PMC8693331 DOI: 10.1259/dmfr.20210197] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
In the last few years, artificial intelligence (AI) research has been rapidly developing and emerging in the field of dental and maxillofacial radiology. Dental radiography, which is commonly used in daily practices, provides an incredibly rich resource for AI development and attracted many researchers to develop its application for various purposes. This study reviewed the applicability of AI for dental radiography from the current studies. Online searches on PubMed and IEEE Xplore databases, up to December 2020, and subsequent manual searches were performed. Then, we categorized the application of AI according to similarity of the following purposes: diagnosis of dental caries, periapical pathologies, and periodontal bone loss; cyst and tumor classification; cephalometric analysis; screening of osteoporosis; tooth recognition and forensic odontology; dental implant system recognition; and image quality enhancement. Current development of AI methodology in each aforementioned application were subsequently discussed. Although most of the reviewed studies demonstrated a great potential of AI application for dental radiography, further development is still needed before implementation in clinical routine due to several challenges and limitations, such as lack of datasets size justification and unstandardized reporting format. Considering the current limitations and challenges, future AI research in dental radiography should follow standardized reporting formats in order to align the research designs and enhance the impact of AI development globally.
Collapse
Affiliation(s)
| | - Chiaki Doi
- Division of Advanced Prosthetic Dentistry, Tohoku University Graduate School of Dentistry, 4–1 Seiryo-machi, Sendai, Japan
| | - Nobuhiro Yoda
- Division of Advanced Prosthetic Dentistry, Tohoku University Graduate School of Dentistry, 4–1 Seiryo-machi, Sendai, Japan
| | - Eha Renwi Astuti
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Jl. Mayjen Prof. Dr. Moestopo no 47, Surabaya, Indonesia
| | - Keiichi Sasaki
- Division of Advanced Prosthetic Dentistry, Tohoku University Graduate School of Dentistry, 4–1 Seiryo-machi, Sendai, Japan
| |
Collapse
|
14
|
Kim YH, Lee C, Ha EG, Choi YJ, Han SS. A fully deep learning model for the automatic identification of cephalometric landmarks. Imaging Sci Dent 2021; 51:299-306. [PMID: 34621657 PMCID: PMC8479429 DOI: 10.5624/isd.20210077] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/24/2021] [Accepted: 05/28/2021] [Indexed: 11/30/2022] Open
Abstract
Purpose This study aimed to propose a fully automatic landmark identification model based on a deep learning algorithm using real clinical data and to verify its accuracy considering inter-examiner variability. Materials and Methods In total, 950 lateral cephalometric images from Yonsei Dental Hospital were used. Two calibrated examiners manually identified the 13 most important landmarks to set as references. The proposed deep learning model has a 2-step structure—a region of interest machine and a detection machine—each consisting of 8 convolution layers, 5 pooling layers, and 2 fully connected layers. The distance errors of detection between 2 examiners were used as a clinically acceptable range for performance evaluation. Results The 13 landmarks were automatically detected using the proposed model. Inter-examiner agreement for all landmarks indicated excellent reliability based on the 95% confidence interval. The average clinically acceptable range for all 13 landmarks was 1.24 mm. The mean radial error between the reference values assigned by 1 expert and the proposed model was 1.84 mm, exhibiting a successful detection rate of 36.1%. The A-point, the incisal tip of the maxillary and mandibular incisors, and ANS showed lower mean radial error than the calibrated expert variability. Conclusion This experiment demonstrated that the proposed deep learning model can perform fully automatic identification of cephalometric landmarks and achieve better results than examiners for some landmarks. It is meaningful to consider between-examiner variability for clinical applicability when evaluating the performance of deep learning methods in cephalometric landmark identification.
Collapse
Affiliation(s)
- Young Hyun Kim
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea
| | - Chena Lee
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea
| | - Eun-Gyu Ha
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea
| | - Yoon Jeong Choi
- Department of Orthodontics, Institute of Craniofacial Deformity, Yonsei University College of Dentistry, Seoul, Korea
| | - Sang-Sun Han
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea.,Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, Korea
| |
Collapse
|
15
|
Bichu YM, Hansa I, Bichu AY, Premjani P, Flores-Mir C, Vaid NR. Applications of artificial intelligence and machine learning in orthodontics: a scoping review. Prog Orthod 2021; 22:18. [PMID: 34219198 PMCID: PMC8255249 DOI: 10.1186/s40510-021-00361-9] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 05/12/2021] [Indexed: 12/15/2022] Open
Abstract
Introduction This scoping review aims to provide an overview of the existing evidence on the use of artificial intelligence (AI) and machine learning (ML) in orthodontics, its translation into clinical practice, and what limitations do exist that have precluded their envisioned application. Methods A scoping review of the literature was carried out following the PRISMA-ScR guidelines. PubMed was searched until July 2020. Results Sixty-two articles fulfilled the inclusion criteria. A total of 43 out of the 62 studies (69.35%) were published this last decade. The majority of these studies were from the USA (11), followed by South Korea (9) and China (7). The number of studies published in non-orthodontic journals (36) was more extensive than in orthodontic journals (26). Artificial Neural Networks (ANNs) were found to be the most commonly utilized AI/ML algorithm (13 studies), followed by Convolutional Neural Networks (CNNs), Support Vector Machine (SVM) (9 studies each), and regression (8 studies). The most commonly studied domains were diagnosis and treatment planning—either broad-based or specific (33), automated anatomic landmark detection and/or analyses (19), assessment of growth and development (4), and evaluation of treatment outcomes (2). The different characteristics and distribution of these studies have been displayed and elucidated upon therein. Conclusion This scoping review suggests that there has been an exponential increase in the number of studies involving various orthodontic applications of AI and ML. The most commonly studied domains were diagnosis and treatment planning, automated anatomic landmark detection and/or analyses, and growth and development assessment. Supplementary Information The online version contains supplementary material available at 10.1186/s40510-021-00361-9.
Collapse
Affiliation(s)
| | | | | | | | - Carlos Flores-Mir
- Department of Orthodontics, University of Alberta, Edmonton, Alberta, Canada
| | - Nikhilesh R Vaid
- Department of Orthodontics, European University College, Dubai, United Arab Emirates
| |
Collapse
|
16
|
|
17
|
Choi YJ, Lee KJ. Possibilities of artificial intelligence use in orthodontic diagnosis and treatment planning: Image recognition and three-dimensional VTO. Semin Orthod 2021. [DOI: 10.1053/j.sodo.2021.05.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
18
|
Kim J, Kim I, Kim YJ, Kim M, Cho JH, Hong M, Kang KH, Lim SH, Kim SJ, Kim YH, Kim N, Sung SJ, Baek SH. Accuracy of automated identification of lateral cephalometric landmarks using cascade convolutional neural networks on lateral cephalograms from nationwide multi-centres. Orthod Craniofac Res 2021; 24 Suppl 2:59-67. [PMID: 33973341 DOI: 10.1111/ocr.12493] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/16/2021] [Accepted: 04/27/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To investigate the accuracy of automated identification of cephalometric landmarks using the cascade convolutional neural networks (CNN) on lateral cephalograms acquired from nationwide multi-centres. SETTINGS AND SAMPLE POPULATION A total of 3150 lateral cephalograms were acquired from 10 university hospitals in South Korea for training. MATERIALS AND METHODS We evaluated the accuracy of the developed model with independent 100 lateral cephalograms as an external validation. Two orthodontists independently identified the anatomic landmarks of the test data set using the V-ceph software (version 8.0, Osstem, Seoul, Korea). The mean positions of the landmarks identified by two orthodontists were regarded as the gold standard. The performance of the CNN model was evaluated by calculating the mean absolute distance between the gold standard and the automatically detected positions. Factors associated with the detection accuracy for landmarks were analysed using the linear regression models. RESULTS The mean inter-examiner difference was 1.31 ± 1.13 mm. The overall automated detection error was 1.36 ± 0.98 mm. The mean detection error for each landmark ranged between 0.46 ± 0.37 mm (maxillary incisor crown tip) and 2.09 ± 1.91 mm (distal root tip of the mandibular first molar). A significant difference in the detection accuracy among cephalograms was noted according to hospital (P = .011), sensor type (P < .01), and cephalography machine model (P < .01). CONCLUSION The automated cephalometric landmark detection model may aid in preliminary screening for patient diagnosis and mid-treatment assessment, independent of the type of the radiography machines tested.
Collapse
Affiliation(s)
- Jaerong Kim
- Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Inhwan Kim
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Yoon-Ji Kim
- Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Minji Kim
- Department of Orthodontics, College of Medicine, Ewha Woman's University, Seoul, Korea
| | - Jin-Hyoung Cho
- Department of Orthodontics, Chonnam National University School of Dentistry, Gwangju, Korea
| | - Mihee Hong
- Department of Orthodontics, School of Dentistry, Kyungpook National University, Daegu, Korea
| | - Kyung-Hwa Kang
- Department of Orthodontics, School of Dentistry, Wonkwang University, Iksan, Korea
| | - Sung-Hoon Lim
- Department of Orthodontics, College of Dentistry, Chosun University, Gwangju, Korea
| | - Su-Jung Kim
- Department of Orthodontics, Kyung Hee University School of Dentistry, Seoul, Korea
| | - Young Ho Kim
- Department of Orthodontics, Institute of Oral Health Science, Ajou University School of Medicine, Suwon, Korea
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sang-Jin Sung
- Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seung-Hak Baek
- Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, Seoul, Korea
| |
Collapse
|
19
|
Influence of the Depth of the Convolutional Neural Networks on an Artificial Intelligence Model for Diagnosis of Orthognathic Surgery. J Pers Med 2021; 11:jpm11050356. [PMID: 33946874 PMCID: PMC8145139 DOI: 10.3390/jpm11050356] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 04/21/2021] [Accepted: 04/24/2021] [Indexed: 12/12/2022] Open
Abstract
The aim of this study was to investigate the relationship between image patterns in cephalometric radiographs and the diagnosis of orthognathic surgery and propose a method to improve the accuracy of predictive models according to the depth of the neural networks. The study included 640 and 320 patients requiring non-surgical and surgical orthodontic treatments, respectively. The data of 150 patients were exclusively classified as a test set. The data of the remaining 810 patients were split into five groups and a five-fold cross-validation was performed. The convolutional neural network models used were ResNet-18, 34, 50, and 101. The number in the model name represents the difference in the depth of the blocks that constitute the model. The accuracy, sensitivity, and specificity of each model were estimated and compared. The average success rate in the test set for the ResNet-18, 34, 50, and 101 was 93.80%, 93.60%, 91.13%, and 91.33%, respectively. In screening, ResNet-18 had the best performance with an area under the curve of 0.979, followed by ResNets-34, 50, and 101 at 0.974, 0.945, and 0.944, respectively. This study suggests the required characteristics of the structure of an artificial intelligence model for decision-making based on medical images.
Collapse
|
20
|
|
21
|
Zeng M, Yan Z, Liu S, Zhou Y, Qiu L. Cascaded convolutional networks for automatic cephalometric landmark detection. Med Image Anal 2020; 68:101904. [PMID: 33290934 DOI: 10.1016/j.media.2020.101904] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2019] [Revised: 06/15/2020] [Accepted: 11/11/2020] [Indexed: 11/17/2022]
Abstract
Cephalometric analysis is a fundamental examination which is widely used in orthodontic diagnosis and treatment planning. Its key step is to detect the anatomical landmarks in lateral cephalograms, which is time-consuming in traditional manual way. To solve this problem, we propose a novel approach with a cascaded three-stage convolutional neural networks to predict cephalometric landmarks automatically. In the first stage, high-level features of the craniofacial structures are extracted to locate the lateral face area which helps to overcome the appearance variations. Next, we process the aligned face area to estimate the locations of all landmarks simultaneously. At the last stage, each landmark is refined through a dedicated network using high-resolution image data around the initial position to achieve more accurate result. We evaluate the proposed method on several anatomical landmark datasets and the experimental results show that our method achieved competitive performance compared with the other methods.
Collapse
Affiliation(s)
- Minmin Zeng
- Fourth Clinical Division, School and Hospital of Stomatology, Peking University, Beijing, China.
| | | | - Shuai Liu
- Second Clinical Division, School and Hospital of Stomatology, Peking University, Beijing, China
| | - Yanheng Zhou
- Department of orthodontics, School and Hospital of Stomatology, Peking University, Beijing, China
| | - Lixin Qiu
- Fourth Clinical Division, School and Hospital of Stomatology, Peking University, Beijing, China
| |
Collapse
|
22
|
Le VL, Beurton-Aimar M, Zemmari A, Marie A, Parisey N. Automated landmarking for insects morphometric analysis using deep neural networks. ECOL INFORM 2020. [DOI: 10.1016/j.ecoinf.2020.101175] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
23
|
Automatic Cephalometric Landmark Detection on X-ray Images Using a Deep-Learning Method. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10072547] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Accurate automatic quantitative cephalometry are essential for orthodontics. However, manual labeling of cephalometric landmarks is tedious and subjective, which also must be performed by professional doctors. In recent years, deep learning has gained attention for its success in computer vision field. It has achieved large progress in resolving problems like image classification or image segmentation. In this paper, we propose a two-step method which can automatically detect cephalometric landmarks on skeletal X-ray images. First, we roughly extract a region of interest (ROI) patch for each landmark by registering the testing image to training images, which have annotated landmarks. Then, we utilize pre-trained networks with a backbone of ResNet50, which is a state-of-the-art convolutional neural network, to detect each landmark in each ROI patch. The network directly outputs the coordinates of the landmarks. We evaluate our method on two datasets: ISBI 2015 Grand Challenge in Dental X-ray Image Analysis and our own dataset provided by Shandong University. The experiments demonstrate that the proposed method can achieve satisfying results on both SDR (Successful Detection Rate) and SCR (Successful Classification Rate). However, the computational time issue remains to be improved in the future.
Collapse
|
24
|
Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofac Radiol 2019; 49:20190107. [PMID: 31386555 DOI: 10.1259/dmfr.20190107] [Citation(s) in RCA: 124] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVES To investigate the current clinical applications and diagnostic performance of artificial intelligence (AI) in dental and maxillofacial radiology (DMFR). METHODS Studies using applications related to DMFR to develop or implement AI models were sought by searching five electronic databases and four selected core journals in the field of DMFR. The customized assessment criteria based on QUADAS-2 were adapted for quality analysis of the studies included. RESULTS The initial electronic search yielded 1862 titles, and 50 studies were eventually included. Most studies focused on AI applications for an automated localization of cephalometric landmarks, diagnosis of osteoporosis, classification/segmentation of maxillofacial cysts and/or tumors, and identification of periodontitis/periapical disease. The performance of AI models varies among different algorithms. CONCLUSION The AI models proposed in the studies included exhibited wide clinical applications in DMFR. Nevertheless, it is still necessary to further verify the reliability and applicability of the AI models prior to transferring these models into clinical practice.
Collapse
Affiliation(s)
- Kuofeng Hung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Carla Montalvao
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Ray Tanaka
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Taisuke Kawai
- Department of Oral and Maxillofacial Radiology, School of Life Dentistry at Tokyo, Nippon Dental University, Tokyo, Japan
| | - Michael M Bornstein
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| |
Collapse
|
25
|
Automatic Analysis of Lateral Cephalograms Based on Multiresolution Decision Tree Regression Voting. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:1797502. [PMID: 30581546 PMCID: PMC6276415 DOI: 10.1155/2018/1797502] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Accepted: 10/22/2018] [Indexed: 11/20/2022]
Abstract
Cephalometric analysis is a standard tool for assessment and prediction of craniofacial growth, orthodontic diagnosis, and oral-maxillofacial treatment planning. The aim of this study is to develop a fully automatic system of cephalometric analysis, including cephalometric landmark detection and cephalometric measurement in lateral cephalograms for malformation classification and assessment of dental growth and soft tissue profile. First, a novel method of multiscale decision tree regression voting using SIFT-based patch features is proposed for automatic landmark detection in lateral cephalometric radiographs. Then, some clinical measurements are calculated by using the detected landmark positions. Finally, two databases are tested in this study: one is the benchmark database of 300 lateral cephalograms from 2015 ISBI Challenge, and the other is our own database of 165 lateral cephalograms. Experimental results show that the performance of our proposed method is satisfactory for landmark detection and measurement analysis in lateral cephalograms.
Collapse
|
26
|
Vandaele R, Aceto J, Muller M, Péronnet F, Debat V, Wang CW, Huang CT, Jodogne S, Martinive P, Geurts P, Marée R. Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach. Sci Rep 2018; 8:538. [PMID: 29323201 PMCID: PMC5765108 DOI: 10.1038/s41598-017-18993-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Accepted: 12/20/2017] [Indexed: 11/23/2022] Open
Abstract
The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research.
Collapse
Affiliation(s)
- Rémy Vandaele
- Montefiore Institute, Department of Electrical engineering and Computer Science., University of Liège, Liège, 4000, Belgium.
| | - Jessica Aceto
- Laboratory for Organogenesis and Regeneration, GIGA-Research, University of Liège, Liège, 4000, Belgium
| | - Marc Muller
- Laboratory for Organogenesis and Regeneration, GIGA-Research, University of Liège, Liège, 4000, Belgium
| | - Frédérique Péronnet
- Institut de Biologie Paris-Seine (IBPS), UMR7622, Laboratoire de Biologie du Développement, UPMC Univ Paris 06, Paris, F-75005, France
| | - Vincent Debat
- Institut de Systématique, Evolution, Biodiversité, ISYEB UMR 7205 (CNRS, MNHN, UPMC, EPHE), Muséum national d'Histoire naturelle, Sorbonne Universités, Paris, F-75005, France
| | - Ching-Wei Wang
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan
| | - Cheng-Ta Huang
- Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan
| | - Sébastien Jodogne
- Department of Medical Physics, University Hospital (CHU) of Liège, University of Liège, Liège, 4000, Belgium
| | - Philippe Martinive
- Department of Medical Physics, University Hospital (CHU) of Liège, University of Liège, Liège, 4000, Belgium
| | - Pierre Geurts
- Montefiore Institute, Department of Electrical engineering and Computer Science., University of Liège, Liège, 4000, Belgium
| | - Raphaël Marée
- Montefiore Institute, Department of Electrical engineering and Computer Science., University of Liège, Liège, 4000, Belgium
| |
Collapse
|
27
|
Arık SÖ, Ibragimov B, Xing L. Fully automated quantitative cephalometry using convolutional neural networks. J Med Imaging (Bellingham) 2017; 4:014501. [PMID: 28097213 PMCID: PMC5220585 DOI: 10.1117/1.jmi.4.1.014501] [Citation(s) in RCA: 107] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Accepted: 12/12/2016] [Indexed: 11/14/2022] Open
Abstract
Quantitative cephalometry plays an essential role in clinical diagnosis, treatment, and surgery. Development of fully automated techniques for these procedures is important to enable consistently accurate computerized analyses. We study the application of deep convolutional neural networks (CNNs) for fully automated quantitative cephalometry for the first time. The proposed framework utilizes CNNs for detection of landmarks that describe the anatomy of the depicted patient and yield quantitative estimation of pathologies in the jaws and skull base regions. We use a publicly available cephalometric x-ray image dataset to train CNNs for recognition of landmark appearance patterns. CNNs are trained to output probabilistic estimations of different landmark locations, which are combined using a shape-based model. We evaluate the overall framework on the test set and compare with other proposed techniques. We use the estimated landmark locations to assess anatomically relevant measurements and classify them into different anatomical types. Overall, our results demonstrate high anatomical landmark detection accuracy ([Formula: see text] to 2% higher success detection rate for a 2-mm range compared with the top benchmarks in the literature) and high anatomical type classification accuracy ([Formula: see text] average classification accuracy for test set). We demonstrate that CNNs, which merely input raw image patches, are promising for accurate quantitative cephalometry.
Collapse
Affiliation(s)
- Sercan Ö. Arık
- Baidu USA, 1195 Bordeaux Drive, Sunnyvale, California 94089, United States
| | - Bulat Ibragimov
- Stanford University, Department of Radiation Oncology, School of Medicine, 875 Blake Wilbur Drive, Stanford, California 94305, United States
| | - Lei Xing
- Stanford University, Department of Radiation Oncology, School of Medicine, 875 Blake Wilbur Drive, Stanford, California 94305, United States
| |
Collapse
|
28
|
Raith S, Varga V, Steiner T, Hölzle F, Fischer H. Computational geometry assessment for morphometric analysis of the mandible. Comput Methods Biomech Biomed Engin 2016; 20:27-34. [DOI: 10.1080/10255842.2016.1196196] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Stefan Raith
- Department of Dental Materials and Biomaterials Research, RWTH Aachen University Hospital, Aachen, Germany
| | - Viktoria Varga
- Department of Oral and Maxillofacial Surgery, RWTH Aachen University Hospital, Aachen, Germany
| | - Timm Steiner
- Department of Oral and Maxillofacial Surgery, RWTH Aachen University Hospital, Aachen, Germany
| | - Frank Hölzle
- Department of Oral and Maxillofacial Surgery, RWTH Aachen University Hospital, Aachen, Germany
| | - Horst Fischer
- Department of Dental Materials and Biomaterials Research, RWTH Aachen University Hospital, Aachen, Germany
| |
Collapse
|
29
|
Tam WK, Lee HJ. Improving point correspondence in cephalograms by using a two-stage rectified point transform. Comput Biol Med 2015; 65:114-23. [DOI: 10.1016/j.compbiomed.2015.07.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2015] [Revised: 07/15/2015] [Accepted: 07/27/2015] [Indexed: 11/15/2022]
|
30
|
Image Segmentation and Analysis of Flexion-Extension Radiographs of Cervical Spines. J Med Eng 2014; 2014:976323. [PMID: 27006937 PMCID: PMC4782582 DOI: 10.1155/2014/976323] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Revised: 09/21/2014] [Accepted: 09/26/2014] [Indexed: 11/21/2022] Open
Abstract
We present a new analysis tool for cervical flexion-extension radiographs based on machine vision and computerized image processing. The method is based on semiautomatic image segmentation leading to detection of common landmarks such as the spinolaminar (SL) line or contour lines of the implanted anterior cervical plates. The technique allows for visualization of the local curvature of these landmarks during flexion-extension experiments. In addition to changes in the curvature of the SL line, it has been found that the cervical plates also deform during flexion-extension examination. While extension radiographs reveal larger curvature changes in the SL line, flexion radiographs on the other hand tend to generate larger curvature changes in the implanted cervical plates. Furthermore, while some lordosis is always present in the cervical plates by design, it actually decreases during extension and increases during flexion. Possible causes of this unexpected finding are also discussed. The described analysis may lead to a more precise interpretation of flexion-extension radiographs, allowing diagnosis of spinal instability and/or pseudoarthrosis in already seemingly fused spines.
Collapse
|
31
|
Le-Tien T, Pham-Chi H. An Approach for Efficient Detection of Cephalometric Landmarks. ACTA ACUST UNITED AC 2014. [DOI: 10.1016/j.procs.2014.08.044] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
32
|
Vucinić P, Trpovski Z, Sćepan I. Automatic landmarking of cephalograms using active appearance models. Eur J Orthod 2010; 32:233-41. [PMID: 20203126 DOI: 10.1093/ejo/cjp099] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
There have been many attempts to further improve and automate cephalometric analysis in order to increase accuracy, reduce errors due to subjectivity, and to provide more efficient use of clinicians' time. The aim of this research was to evaluate an automated system for landmarking of cephalograms based on the use of an active appearance model (AAM) that contains a statistical model of shape and grey-level appearance of an object of interest and represents both shape and texture variations of the region covered by the model. Multi-resolution implementation was used, in which the AAM iterate to convergence at each level before projecting the current solution to the next level of the model. The AAM system was trained using 60 randomly selected, hand-annotated digital cephalograms of subjects between 7.2 and 25.6 years of age, and tested with a leave-five-out method that enabled testing not only of the accuracy of the AAM system but also the accuracy of each AAM. Differences between methods were examined using the non-parametric Wilcoxon signed rank test. An average accuracy of 1.68 mm was obtained, with 61 per cent of landmarks detected within 2 mm and 95 per cent of landmarks detected within 5 mm precision. A noticeable increase in overall precision and detection of low-contrast cephalometric landmarks was achieved compared with other automated systems. These results suggest that the AAM approach can adequately represent the average shape and texture variations of craniofacial structures on digital radiographs. As such it can successfully be implemented for automatic localization of cephalometric landmarks.
Collapse
MESH Headings
- Adolescent
- Adult
- Algorithms
- Cephalometry/methods
- Cephalometry/statistics & numerical data
- Child
- Face/anatomy & histology
- Facial Bones/anatomy & histology
- Female
- Humans
- Image Interpretation, Computer-Assisted/methods
- Image Processing, Computer-Assisted/methods
- Image Processing, Computer-Assisted/statistics & numerical data
- Male
- Malocclusion, Angle Class I/diagnostic imaging
- Malocclusion, Angle Class II/diagnostic imaging
- Malocclusion, Angle Class III/diagnostic imaging
- Models, Statistical
- Pattern Recognition, Automated/methods
- Pattern Recognition, Automated/statistics & numerical data
- Radiography, Dental, Digital/methods
- Radiography, Dental, Digital/statistics & numerical data
- Young Adult
Collapse
Affiliation(s)
- Predrag Vucinić
- Department of Orthodontics, University of Novi Sad, Novi Sad, Serbia.
| | | | | |
Collapse
|
33
|
Tanikawa C, Yagi M, Takada K. Automated cephalometry: system performance reliability using landmark-dependent criteria. Angle Orthod 2010; 79:1037-46. [PMID: 19852592 DOI: 10.2319/092908-508r.1] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE The purpose of the present study was to evaluate reliability of a system that performs automatic recognition of anatomic landmarks and adjacent structures on lateral cephalograms using landmark-dependent criteria unique to each landmark. MATERIALS AND METHODS To evaluate the reliability of the system, the system was used to examine 65 lateral cephalograms. The area of each system-identified anatomic structure surrounding the landmark and the position of each system-identified landmark were compared with norms using confidence ellipses with alpha = .01, which were derived from the scattergrams of 100 estimates obtained according to the method reported by Baumrind and Frantz. When the system-identified area overlapped with the norm area, anatomic structure recognition was considered successful. In addition, when the system-identified point was located within the norm area, landmark identification was considered successful. Based on these judgment criteria, success rates were calculated for all landmarks. RESULTS The system successfully identified all specified anatomic structures in all the images and determined the positions of the landmarks with a mean success rate of 88% (range, 77%- 100%). CONCLUSION With the incorporation of the rational assessment criteria provided by confidence ellipses, the proposed system was confirmed to be reliable.
Collapse
Affiliation(s)
- Chihiro Tanikawa
- Department of Orthodontics and Dentofacial Orthopedics, Graduate School of Dentistry, Osaka, Japan
| | | | | |
Collapse
|
34
|
Favaedi L, Petrou M. Cephalometric landmarks identification using probabilistic relaxation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2010; 2010:4391-4394. [PMID: 21096647 DOI: 10.1109/iembs.2010.5627141] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
In this paper, we develop methodology to locate cephalometric landmarks on X-ray images based on probabilistic relaxation, which combines local contextual information from the general shape of the bones of the head (used as measurements specific to each landmark in the form of its shape context) and relational information, expressing the relative position of the landmarks with respect to each other.
Collapse
Affiliation(s)
- Leila Favaedi
- Department of Electrical and Electronic Engineering, Communications and Signal Processing Group, Imperial College, London, SW7 2AZ, UK
| | | |
Collapse
|
35
|
An evaluation of cellular neural networks for the automatic identification of cephalometric landmarks on digital images. J Biomed Biotechnol 2009; 2009:717102. [PMID: 19753320 PMCID: PMC2742650 DOI: 10.1155/2009/717102] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2009] [Revised: 05/16/2009] [Accepted: 06/18/2009] [Indexed: 11/17/2022] Open
Abstract
Several efforts have been made to completely automate cephalometric analysis by automatic landmark search. However, accuracy obtained was worse than manual identification in every study. The analogue-to-digital conversion of X-ray has been claimed to be the main problem. Therefore the aim of this investigation was to evaluate the accuracy of the Cellular Neural Networks approach for automatic location of cephalometric landmarks on softcopy of direct digital cephalometric X-rays. Forty-one, direct-digital lateral cephalometric radiographs were obtained by a Siemens Orthophos DS Ceph and were used in this study and 10 landmarks (N, A Point, Ba, Po, Pt, B Point, Pg, PM, UIE, LIE) were the object of automatic landmark identification. The mean errors and standard deviations from the best estimate of cephalometric points were calculated for each landmark. Differences in the mean errors of automatic and manual landmarking were compared with a 1-way analysis of variance. The analyses indicated that the differences were very small, and they were found at most within 0.59 mm. Furthermore, only few of these differences were statistically significant, but differences were so small to be in most instances clinically meaningless. Therefore the use of X-ray files with respect to scanned X-ray improved landmark accuracy of automatic detection. Investigations on softcopy of digital cephalometric X-rays, to search more landmarks in order to enable a complete automatic cephalometric analysis, are strongly encouraged.
Collapse
|
36
|
Nooranipour M, Farahani RM. Estimation of cranial capacity and brain weight in 18–22-year-old Iranian adults. Clin Neurol Neurosurg 2008; 110:997-1002. [DOI: 10.1016/j.clineuro.2008.06.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2008] [Revised: 06/12/2008] [Accepted: 06/14/2008] [Indexed: 10/21/2022]
|
37
|
Leonardi R, Giordano D, Maiorana F, Spampinato C. Automatic cephalometric analysis. Angle Orthod 2008; 78:145-51. [PMID: 18193970 DOI: 10.2319/120506-491.1] [Citation(s) in RCA: 58] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2006] [Accepted: 02/01/2007] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE To describe the techniques used for automatic landmarking of cephalograms, highlighting the strengths and weaknesses of each one and reviewing the percentage of success in locating each cephalometric point. MATERIALS AND METHODS The literature survey was performed by searching the Medline, the Institute of Electrical and Electronics Engineers, and the ISI Web of Science Citation Index databases. The survey covered the period from January 1966 to August 2006. Abstracts that appeared to fulfill the initial selection criteria were selected by consensus. The original articles were then retrieved. Their references were also hand-searched for possible missing articles. The search strategy resulted in 118 articles of which eight met the inclusion criteria. Many articles were rejected for different reasons; among these, the most frequent was that results of accuracy for automatic landmark recognition were presented as a percentage of success. RESULTS A marked difference in results was found between the included studies consisting of heterogeneity in the performance of techniques to detect the same landmark. All in all, hybrid approaches detected cephalometric points with a higher accuracy in contrast to the results for the same points obtained by the model-based, image filtering plus knowledge-based landmark search and "soft-computing" approaches. CONCLUSIONS The systems described in the literature are not accurate enough to allow their use for clinical purposes. Errors in landmark detection were greater than those expected with manual tracing and, therefore, the scientific evidence supporting the use of automatic landmarking is low.
Collapse
Affiliation(s)
- Rosalia Leonardi
- Department of Orthodontics, University of Catania, University of Catania, Catania, Italy.
| | | | | | | |
Collapse
|
38
|
El-Fegh I, Galhood M, Sid-Ahmed M, Ahmadi M. Automated 2-D cephalometric analysis of X-ray by image registration approach based on least square approximator. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2008; 2008:3949-3952. [PMID: 19163577 DOI: 10.1109/iembs.2008.4650074] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
This paper presents a new approach to cephalometric x-ray landmark localization. This approach is based on extracting features from face profile of the lateral skull x-ray. The extracted side profile is then segmented based on knowledge of known local facial structures. Once the face profile is properly segmented, nine perceptually significant landmarks (fiducial points) on the face are registered based on a local maximum curvature computation. These fiducial points are used for point-by-point correspondence to find the transformation coefficients of the control points between two images of a scene. Next, we use the obtained coefficients to find the locations of other landmarks on the target image by mapping the landmarks of the reference image. The algorithm was tested on more than 80 x-ray images to locate 20 landmarks. It was possible to locate the landmarks with accuracy of +/-2mm on more 90% of the landmarks.
Collapse
Affiliation(s)
- I El-Fegh
- Higher Institute of Industry, Misurata-Libya.
| | | | | | | |
Collapse
|
39
|
Abstract
LEARNING OBJECTIVES After studying this article, the participant should be able to: 1. Describe the historical origins of modern cephalometry. 2. Identify common landmark points on the lateral cephalogram. 3. Describe multiple common clinical uses for cephalometry. 4. Exhibit knowledge of developments in imaging and analysis alternatives. BACKGROUND Interest in the dimensions of the human head has been present since antiquity. Proportional analysis and measures from cadaveric specimens led to the development of radiologic image capture and analysis on living subjects. These techniques were originally applied to establishing normative values, documenting growth, and diagnosing dentofacial disharmonies. This article reviews the origins of cephalometric methodology and current developments and applications. METHODS The authors conducted a MEDLINE search and review of all English language articles using the keywords "cephalometric" and "cephalometrics." RESULTS Cephalometrics have undergone substantial use and development since the introduction of radiologic imaging on living human subjects in 1931. Although frequently associated with orthognathic surgery, cephalometrics have been applied to a number of conditions involving altered craniofacial morphology. Advances in imaging and computing have led to increased interest in three-dimensional and non-x-ray-based assessment of the human head. Mathematical models have been applied to standard cephalometric information to increase the descriptive accuracy of the complex shapes involved. CONCLUSIONS Cephalometric techniques and analyses are versatile tools that can be applied to a wide variety of clinical scenarios involving the craniofacial region. New technologies and expanded applications promise to continue the development and use of this well-established methodology.
Collapse
|
40
|
Rueda S, Alcañiz M. An approach for the automatic cephalometric landmark detection using mathematical morphology and active appearance models. ACTA ACUST UNITED AC 2007; 9:159-66. [PMID: 17354886 DOI: 10.1007/11866565_20] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Cephalometric analysis of lateral radiographs of the head is an important diagnosis tool in orthodontics. Based on manually locating specific landmarks, it is a tedious, time-consuming and error prone task. In this paper, we propose an automated system based on the use of Active Appearance Models (AAMs). Special attention has been paid to clinical validation of our method since previous work in this field used few images, was tested in the training set and/or did not take into account the variability of the images. In this research, a top-hat transformation was used to correct the intensity inhomogeneity of the radiographs generating a consistent training set that overcomes the above described drawbacks. The AAM was trained using 96 hand-annotated images and tested with a leave-one-out scheme obtaining an average accuracy of 2.48mm. Results show that AAM combined with mathematical morphology is the suitable method for clinical cephalometric applications.
Collapse
Affiliation(s)
- Sylvia Rueda
- Medical Image Computing Laboratory, Universidad Politécnica de Valencia, UPV/ETSIA, Camino de Vera s/n, 46022 Valencia, Spain.
| | | |
Collapse
|
41
|
El-Feghi I, Sid-Ahmed M, Ahmadi M. Automatic localization of craniofacial landmarks using multi-layer perceptron as a function approximator. Pattern Recognit Lett 2006. [DOI: 10.1016/j.patrec.2005.09.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
42
|
|
43
|
Douglas TS. Image processing for craniofacial landmark identification and measurement: a review of photogrammetry and cephalometry. Comput Med Imaging Graph 2004; 28:401-9. [PMID: 15464879 DOI: 10.1016/j.compmedimag.2004.06.002] [Citation(s) in RCA: 65] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2004] [Accepted: 06/18/2004] [Indexed: 11/20/2022]
Abstract
Facial surface anthropometry and cephalometry have been used for many years for the diagnosis of malformations, surgical planning and evaluation, and growth studies. These disciplines rely on the identification of craniofacial landmarks. Methods for 3D reconstruction of landmarks have been introduced, as have image processing algorithms for the automation of landmark extraction. This paper reviews facial surface anthropometry and cephalometry with reference to the image processing algorithms that have been applied and their effectiveness.
Collapse
Affiliation(s)
- Tania S Douglas
- MRC/UCT Medical Imaging Research Unit, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Observatory 7925, South Africa.
| |
Collapse
|