1
|
Liu X, Qu L, Xie Z, Zhao J, Shi Y, Song Z. Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation. Biomed Eng Online 2024; 23:52. [PMID: 38851691 PMCID: PMC11162022 DOI: 10.1186/s12938-024-01238-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 04/11/2024] [Indexed: 06/10/2024] Open
Abstract
Accurate segmentation of multiple organs in the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feature extraction approach and end-to-end training, automatic deep learning-based multi-organ segmentation methods have far outperformed traditional methods and become a new research topic. This review systematically summarizes the latest research in this field. We searched Google Scholar for papers published from January 1, 2016 to December 31, 2023, using keywords "multi-organ segmentation" and "deep learning", resulting in 327 papers. We followed the PRISMA guidelines for paper selection, and 195 studies were deemed to be within the scope of this review. We summarized the two main aspects involved in multi-organ segmentation: datasets and methods. Regarding datasets, we provided an overview of existing public datasets and conducted an in-depth analysis. Concerning methods, we categorized existing approaches into three major classes: fully supervised, weakly supervised and semi-supervised, based on whether they require complete label information. We summarized the achievements of these methods in terms of segmentation accuracy. In the discussion and conclusion section, we outlined and summarized the current trends in multi-organ segmentation.
Collapse
Affiliation(s)
- Xiaoyu Liu
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, 138 Yixueyuan Road, Shanghai, 200032, People's Republic of China
- Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai, 200032, China
| | - Linhao Qu
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, 138 Yixueyuan Road, Shanghai, 200032, People's Republic of China
- Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai, 200032, China
| | - Ziyue Xie
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, 138 Yixueyuan Road, Shanghai, 200032, People's Republic of China
- Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai, 200032, China
| | - Jiayue Zhao
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, 138 Yixueyuan Road, Shanghai, 200032, People's Republic of China
- Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai, 200032, China
| | - Yonghong Shi
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, 138 Yixueyuan Road, Shanghai, 200032, People's Republic of China.
- Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai, 200032, China.
| | - Zhijian Song
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, 138 Yixueyuan Road, Shanghai, 200032, People's Republic of China.
- Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai, 200032, China.
| |
Collapse
|
2
|
Kakkos I, Vagenas TP, Zygogianni A, Matsopoulos GK. Towards Automation in Radiotherapy Planning: A Deep Learning Approach for the Delineation of Parotid Glands in Head and Neck Cancer. Bioengineering (Basel) 2024; 11:214. [PMID: 38534488 DOI: 10.3390/bioengineering11030214] [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: 12/27/2023] [Revised: 02/19/2024] [Accepted: 02/22/2024] [Indexed: 03/28/2024] Open
Abstract
The delineation of parotid glands in head and neck (HN) carcinoma is critical to assess radiotherapy (RT) planning. Segmentation processes ensure precise target position and treatment precision, facilitate monitoring of anatomical changes, enable plan adaptation, and enhance overall patient safety. In this context, artificial intelligence (AI) and deep learning (DL) have proven exceedingly effective in precisely outlining tumor tissues and, by extension, the organs at risk. This paper introduces a DL framework using the AttentionUNet neural network for automatic parotid gland segmentation in HN cancer. Extensive evaluation of the model is performed in two public and one private dataset, while segmentation accuracy is compared with other state-of-the-art DL segmentation schemas. To assess replanning necessity during treatment, an additional registration method is implemented on the segmentation output, aligning images of different modalities (Computed Tomography (CT) and Cone Beam CT (CBCT)). AttentionUNet outperforms similar DL methods (Dice Similarity Coefficient: 82.65% ± 1.03, Hausdorff Distance: 6.24 mm ± 2.47), confirming its effectiveness. Moreover, the subsequent registration procedure displays increased similarity, providing insights into the effects of RT procedures for treatment planning adaptations. The implementation of the proposed methods indicates the effectiveness of DL not only for automatic delineation of the anatomical structures, but also for the provision of information for adaptive RT support.
Collapse
Affiliation(s)
- Ioannis Kakkos
- Biomedical Engineering Laboratory, National Technical University of Athens, 15773 Athens, Greece
| | - Theodoros P Vagenas
- Biomedical Engineering Laboratory, National Technical University of Athens, 15773 Athens, Greece
| | - Anna Zygogianni
- Radiation Oncology Unit, 1st Department of Radiology, ARETAIEION University Hospital, 11528 Athens, Greece
| | - George K Matsopoulos
- Biomedical Engineering Laboratory, National Technical University of Athens, 15773 Athens, Greece
| |
Collapse
|
3
|
Luan S, Ding Y, Shao J, Zou B, Yu X, Qin N, Zhu B, Wei W, Xue X. Deep learning for head and neck semi-supervised semantic segmentation. Phys Med Biol 2024; 69:055008. [PMID: 38306968 DOI: 10.1088/1361-6560/ad25c2] [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: 09/15/2023] [Accepted: 02/01/2024] [Indexed: 02/04/2024]
Abstract
Objective. Radiation therapy (RT) represents a prevalent therapeutic modality for head and neck (H&N) cancer. A crucial phase in RT planning involves the precise delineation of organs-at-risks (OARs), employing computed tomography (CT) scans. Nevertheless, the manual delineation of OARs is a labor-intensive process, necessitating individual scrutiny of each CT image slice, not to mention that a standard CT scan comprises hundreds of such slices. Furthermore, there is a significant domain shift between different institutions' H&N data, which makes traditional semi-supervised learning strategies susceptible to confirmation bias. Therefore, effectively using unlabeled datasets to support annotated datasets for model training has become a critical issue for preventing domain shift and confirmation bias.Approach. In this work, we proposed an innovative cross-domain orthogon-based-perspective consistency (CD-OPC) strategy within a two-branch collaborative training framework, which compels the two sub-networks to acquire valuable features from unrelated perspectives. More specifically, a novel generative pretext task cross-domain prediction (CDP) was designed for learning inherent properties of CT images. Then this prior knowledge was utilized to promote the independent learning of distinct features by the two sub-networks from identical inputs, thereby enhancing the perceptual capabilities of the sub-networks through orthogon-based pseudo-labeling knowledge transfer.Main results. Our CD-OPC model was trained on H&N datasets from nine different institutions, and validated on the four local intuitions' H&N datasets. Among all datasets CD-OPC achieved more advanced performance than other semi-supervised semantic segmentation algorithms.Significance. The CD-OPC method successfully mitigates domain shift and prevents network collapse. In addition, it enhances the network's perceptual abilities, and generates more reliable predictions, thereby further addressing the confirmation bias issue.
Collapse
Affiliation(s)
- Shunyao Luan
- School of Integrated Circuits, Laboratory for optoelectronics, Huazhong University of Science and Technology, Wuhan, People's Republic of China
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Yi Ding
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Jiakang Shao
- School of Integrated Circuits, Laboratory for optoelectronics, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Bing Zou
- Department of Oncology, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China
| | - Xiao Yu
- Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People's Republic of China
| | - Nannan Qin
- The First Affiliated Hospital of Bengbu Medical College, Bengbu, People's Republic of China
| | - Benpeng Zhu
- School of Integrated Circuits, Laboratory for optoelectronics, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Wei Wei
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Xudong Xue
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| |
Collapse
|
4
|
Wang Y, Wang J, Zhou W, Liu Z, Yang C. MAUNext: a lightweight segmentation network for medical images. Phys Med Biol 2023; 68:235003. [PMID: 37931318 DOI: 10.1088/1361-6560/ad0a1f] [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: 04/28/2023] [Accepted: 11/06/2023] [Indexed: 11/08/2023]
Abstract
Objective. The primary objective of this study is to enhance medical image segmentation techniques for clinical research by prioritizing accuracy and the number of parameters.Approach. To achieve this objective, a novel codec-based MAUNext approach is devised, focusing on lightweight backbone design and the integration of skip connections utilizing multiscale, attention mechanism, and other strategic components. The approach is composed of three core modules: a multi-scale attentional convolution module for improved accuracy and parameter reduction, a collaborative neighbourhood-attention MLP encoding module to enhance segmentation performance, and a tiny skip-connected cross-layer semantic fusion module to bridge the semantic gap between encoder and decoder.Main results. The study extensively evaluates the MAUNext approach alongside eight state-of-the-art methods on three renowned datasets: Kagglelung, ISIC, and Brain. The experimental outcomes robustly demonstrate that the proposed approach surpasses other methods in terms of both parameter numbers and accuracy. This achievement holds promise for effectively addressing medical image segmentation tasks.Significance. Automated medical image segmentation, particularly in organ and lesion identification, plays a pivotal role in clinical diagnosis and treatment. Manual segmentation is resource-intensive, thus automated methods are highly valuable. The study underscores the clinical significance of automated segmentation by providing an advanced solution through the innovative MAUNext approach. This approach offers substantial improvements in accuracy and efficiency, which can significantly aid clinical decision-making and patient treatment.
Collapse
Affiliation(s)
- Yuhang Wang
- Power Systems Engineering Research Center, Ministry of Education, College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, People's Republic of China
| | - Jihong Wang
- Power Systems Engineering Research Center, Ministry of Education, College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, People's Republic of China
| | - Wen Zhou
- Power Systems Engineering Research Center, Ministry of Education, College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, People's Republic of China
| | - Zijie Liu
- Power Systems Engineering Research Center, Ministry of Education, College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, People's Republic of China
| | - Chen Yang
- Power Systems Engineering Research Center, Ministry of Education, College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, People's Republic of China
| |
Collapse
|
5
|
Liu P, Sun Y, Zhao X, Yan Y. Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis. Biomed Eng Online 2023; 22:104. [PMID: 37915046 PMCID: PMC10621161 DOI: 10.1186/s12938-023-01159-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 09/21/2023] [Indexed: 11/03/2023] Open
Abstract
PURPOSE The contouring of organs at risk (OARs) in head and neck cancer radiation treatment planning is a crucial, yet repetitive and time-consuming process. Recent studies have applied deep learning (DL) algorithms to automatically contour head and neck OARs. This study aims to conduct a systematic review and meta-analysis to summarize and analyze the performance of DL algorithms in contouring head and neck OARs. The objective is to assess the advantages and limitations of DL algorithms in contour planning of head and neck OARs. METHODS This study conducted a literature search of Pubmed, Embase and Cochrane Library databases, to include studies related to DL contouring head and neck OARs, and the dice similarity coefficient (DSC) of four categories of OARs from the results of each study are selected as effect sizes for meta-analysis. Furthermore, this study conducted a subgroup analysis of OARs characterized by image modality and image type. RESULTS 149 articles were retrieved, and 22 studies were included in the meta-analysis after excluding duplicate literature, primary screening, and re-screening. The combined effect sizes of DSC for brainstem, spinal cord, mandible, left eye, right eye, left optic nerve, right optic nerve, optic chiasm, left parotid, right parotid, left submandibular, and right submandibular are 0.87, 0.83, 0.92, 0.90, 0.90, 0.71, 0.74, 0.62, 0.85, 0.85, 0.82, and 0.82, respectively. For subgroup analysis, the combined effect sizes for segmentation of the brainstem, mandible, left optic nerve, and left parotid gland using CT and MRI images are 0.86/0.92, 0.92/0.90, 0.71/0.73, and 0.84/0.87, respectively. Pooled effect sizes using 2D and 3D images of the brainstem, mandible, left optic nerve, and left parotid gland for contouring are 0.88/0.87, 0.92/0.92, 0.75/0.71 and 0.87/0.85. CONCLUSIONS The use of automated contouring technology based on DL algorithms is an essential tool for contouring head and neck OARs, achieving high accuracy, reducing the workload of clinical radiation oncologists, and providing individualized, standardized, and refined treatment plans for implementing "precision radiotherapy". Improving DL performance requires the construction of high-quality data sets and enhancing algorithm optimization and innovation.
Collapse
Affiliation(s)
- Peiru Liu
- General Hospital of Northern Theater Command, Department of Radiation Oncology, Shenyang, China
- Beifang Hospital of China Medical University, Shenyang, China
| | - Ying Sun
- General Hospital of Northern Theater Command, Department of Radiation Oncology, Shenyang, China
| | - Xinzhuo Zhao
- Shenyang University of Technology, School of Electrical Engineering,, Shenyang, China
| | - Ying Yan
- General Hospital of Northern Theater Command, Department of Radiation Oncology, Shenyang, China.
| |
Collapse
|
6
|
Lin YC, Lin G, Pandey S, Yeh CH, Wang JJ, Lin CY, Ho TY, Ko SF, Ng SH. Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning. Eur Radiol 2023; 33:6548-6556. [PMID: 37338554 PMCID: PMC10415433 DOI: 10.1007/s00330-023-09827-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 03/29/2023] [Accepted: 04/14/2023] [Indexed: 06/21/2023]
Abstract
OBJECTIVES To use convolutional neural network for fully automated segmentation and radiomics features extraction of hypopharyngeal cancer (HPC) tumor in MRI. METHODS MR images were collected from 222 HPC patients, among them 178 patients were used for training, and another 44 patients were recruited for testing. U-Net and DeepLab V3 + architectures were used for training the models. The model performance was evaluated using the dice similarity coefficient (DSC), Jaccard index, and average surface distance. The reliability of radiomics parameters of the tumor extracted by the models was assessed using intraclass correlation coefficient (ICC). RESULTS The predicted tumor volumes by DeepLab V3 + model and U-Net model were highly correlated with those delineated manually (p < 0.001). The DSC of DeepLab V3 + model was significantly higher than that of U-Net model (0.77 vs 0.75, p < 0.05), particularly in those small tumor volumes of < 10 cm3 (0.74 vs 0.70, p < 0.001). For radiomics extraction of the first-order features, both models exhibited high agreement (ICC: 0.71-0.91) with manual delineation. The radiomics extracted by DeepLab V3 + model had significantly higher ICCs than those extracted by U-Net model for 7 of 19 first-order features and for 8 of 17 shape-based features (p < 0.05). CONCLUSION Both DeepLab V3 + and U-Net models produced reasonable results in automated segmentation and radiomic features extraction of HPC on MR images, whereas DeepLab V3 + had a better performance than U-Net. CLINICAL RELEVANCE STATEMENT The deep learning model, DeepLab V3 + , exhibited promising performance in automated tumor segmentation and radiomics extraction for hypopharyngeal cancer on MRI. This approach holds great potential for enhancing the radiotherapy workflow and facilitating prediction of treatment outcomes. KEY POINTS • DeepLab V3 + and U-Net models produced reasonable results in automated segmentation and radiomic features extraction of HPC on MR images. • DeepLab V3 + model was more accurate than U-Net in automated segmentation, especially on small tumors. • DeepLab V3 + exhibited higher agreement for about half of the first-order and shape-based radiomics features than U-Net.
Collapse
Affiliation(s)
- Yu-Chun Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan, 33382, Taiwan
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
- Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Gigin Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan, 33382, Taiwan
- Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Sumit Pandey
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan, 33382, Taiwan
| | - Chih-Hua Yeh
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan, 33382, Taiwan
| | - Jiun-Jie Wang
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
| | - Chien-Yu Lin
- Department of Radiation Oncology, Chang Gung Memorial Hospital at Linkou and Chang Gung University, Taoyuan, Taiwan
| | - Tsung-Ying Ho
- Department of Nuclear Medicine and Molecular Imaging Center, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan
| | - Sheung-Fat Ko
- Department of Radiology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Shu-Hang Ng
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan, 33382, Taiwan.
| |
Collapse
|
7
|
Franzese C, Dei D, Lambri N, Teriaca MA, Badalamenti M, Crespi L, Tomatis S, Loiacono D, Mancosu P, Scorsetti M. Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review. J Pers Med 2023; 13:946. [PMID: 37373935 DOI: 10.3390/jpm13060946] [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: 05/05/2023] [Revised: 06/01/2023] [Accepted: 06/01/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND Head and neck cancer (HNC) is characterized by complex-shaped tumors and numerous organs at risk (OARs), inducing challenging radiotherapy (RT) planning, optimization, and delivery. In this review, we provided a thorough description of the applications of artificial intelligence (AI) tools in the HNC RT process. METHODS The PubMed database was queried, and a total of 168 articles (2016-2022) were screened by a group of experts in radiation oncology. The group selected 62 articles, which were subdivided into three categories, representing the whole RT workflow: (i) target and OAR contouring, (ii) planning, and (iii) delivery. RESULTS The majority of the selected studies focused on the OARs segmentation process. Overall, the performance of AI models was evaluated using standard metrics, while limited research was found on how the introduction of AI could impact clinical outcomes. Additionally, papers usually lacked information about the confidence level associated with the predictions made by the AI models. CONCLUSIONS AI represents a promising tool to automate the RT workflow for the complex field of HNC treatment. To ensure that the development of AI technologies in RT is effectively aligned with clinical needs, we suggest conducting future studies within interdisciplinary groups, including clinicians and computer scientists.
Collapse
Affiliation(s)
- Ciro Franzese
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Damiano Dei
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Nicola Lambri
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Maria Ausilia Teriaca
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Marco Badalamenti
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Leonardo Crespi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
- Centre for Health Data Science, Human Technopole, 20157 Milan, Italy
| | - Stefano Tomatis
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Daniele Loiacono
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
| | - Pietro Mancosu
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Marta Scorsetti
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, Rozzano, 20089 Milan, Italy
| |
Collapse
|
8
|
Zhang Y, Chen C, Huang W, Teng Y, Shu X, Zhao F, Xu J, Zhang L. Preoperative volume of the optic chiasm is an easily obtained predictor for visual recovery of pituitary adenoma patients following endoscopic endonasal transsphenoidal surgery: a cohort study. Int J Surg 2023; 109:896-904. [PMID: 36999782 PMCID: PMC10389445 DOI: 10.1097/js9.0000000000000357] [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: 12/28/2022] [Accepted: 03/13/2023] [Indexed: 04/01/2023]
Abstract
BACKGROUND Predicting the postoperative visual outcome of pituitary adenoma patients is important but remains challenging. This study aimed to identify a novel prognostic predictor which can be automatically obtained from routine MRI using a deep learning approach. MATERIALS AND METHODS A total of 220 pituitary adenoma patients were prospectively enrolled and stratified into the recovery and nonrecovery groups according to the visual outcome at 6 months after endoscopic endonasal transsphenoidal surgery. The optic chiasm was manually segmented on preoperative coronal T2WI, and its morphometric parameters were measured, including suprasellar extension distance, chiasmal thickness, and chiasmal volume. Univariate and multivariate analyses were conducted on clinical and morphometric parameters to identify predictors for visual recovery. Additionally, a deep learning model for automated segmentation and volumetric measurement of optic chiasm was developed with nnU-Net architecture and evaluated in a multicenter data set covering 1026 pituitary adenoma patients from four institutions. RESULTS Larger preoperative chiasmal volume was significantly associated with better visual outcomes ( P =0.001). Multivariate logistic regression suggested it could be taken as the independent predictor for visual recovery (odds ratio=2.838, P <0.001). The auto-segmentation model represented good performances and generalizability in internal (Dice=0.813) and three independent external test sets (Dice=0.786, 0.818, and 0.808, respectively). Moreover, the model achieved accurate volumetric evaluation of the optic chiasm with an intraclass correlation coefficient of more than 0.83 in both internal and external test sets. CONCLUSION The preoperative volume of the optic chiasm could be utilized as the prognostic predictor for visual recovery of pituitary adenoma patients after surgery. Moreover, the proposed deep learning-based model allowed for automated segmentation and volumetric measurement of the optic chiasm on routine MRI.
Collapse
Affiliation(s)
- Yang Zhang
- Department of Neurosurgery, West China Hospital, Sichuan University
- Department of Radiology, West China Hospital, Sichuan University
| | - Chaoyue Chen
- Department of Neurosurgery, West China Hospital, Sichuan University
- Department of Radiology, West China Hospital, Sichuan University
| | - Wei Huang
- College of Computer Science, Sichuan University
| | - Yuen Teng
- Department of Neurosurgery, West China Hospital, Sichuan University
- Department of Radiology, West China Hospital, Sichuan University
| | - Xin Shu
- College of Computer Science, Sichuan University
| | - Fumin Zhao
- Department of Radiology, West China Second University Hospital, Sichuan University
| | - Jianguo Xu
- Department of Neurosurgery, West China Hospital, Sichuan University
- Department of Radiology, West China Hospital, Sichuan University
| | - Lei Zhang
- College of Computer Science, Sichuan University
| |
Collapse
|
9
|
Cubero L, Castelli J, Simon A, de Crevoisier R, Acosta O, Pascau J. Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data. ENTROPY (BASEL, SWITZERLAND) 2022; 24:e24111661. [PMID: 36421515 PMCID: PMC9689629 DOI: 10.3390/e24111661] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/28/2022] [Accepted: 11/09/2022] [Indexed: 06/06/2023]
Abstract
Radiotherapy is one of the main treatments for localized head and neck (HN) cancer. To design a personalized treatment with reduced radio-induced toxicity, accurate delineation of organs at risk (OAR) is a crucial step. Manual delineation is time- and labor-consuming, as well as observer-dependent. Deep learning (DL) based segmentation has proven to overcome some of these limitations, but requires large databases of homogeneously contoured image sets for robust training. However, these are not easily obtained from the standard clinical protocols as the OARs delineated may vary depending on the patient's tumor site and specific treatment plan. This results in incomplete or partially labeled data. This paper presents a solution to train a robust DL-based automated segmentation tool exploiting a clinical partially labeled dataset. We propose a two-step workflow for OAR segmentation: first, we developed longitudinal OAR-specific 3D segmentation models for pseudo-contour generation, completing the missing contours for some patients; with all OAR available, we trained a multi-class 3D convolutional neural network (nnU-Net) for final OAR segmentation. Results obtained in 44 independent datasets showed superior performance of the proposed methodology for the segmentation of fifteen OARs, with an average Dice score coefficient and surface Dice similarity coefficient of 80.59% and 88.74%. We demonstrated that the model can be straightforwardly integrated into the clinical workflow for standard and adaptive radiotherapy.
Collapse
Affiliation(s)
- Lucía Cubero
- Departamento de Bioingeniería, Universidad Carlos III de Madrid, 28911 Madrid, Spain
- Université Rennes, CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, F-35000 Rennes, France
| | - Joël Castelli
- Université Rennes, CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, F-35000 Rennes, France
| | - Antoine Simon
- Université Rennes, CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, F-35000 Rennes, France
| | - Renaud de Crevoisier
- Université Rennes, CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, F-35000 Rennes, France
| | - Oscar Acosta
- Université Rennes, CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, F-35000 Rennes, France
| | - Javier Pascau
- Departamento de Bioingeniería, Universidad Carlos III de Madrid, 28911 Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain
| |
Collapse
|
10
|
Xu J, Zeng B, Egger J, Wang C, Smedby Ö, Jiang X, Chen X. A review on AI-based medical image computing in head and neck surgery. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac840f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 07/25/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Head and neck surgery is a fine surgical procedure with a complex anatomical space, difficult operation and high risk. Medical image computing (MIC) that enables accurate and reliable preoperative planning is often needed to reduce the operational difficulty of surgery and to improve patient survival. At present, artificial intelligence, especially deep learning, has become an intense focus of research in MIC. In this study, the application of deep learning-based MIC in head and neck surgery is reviewed. Relevant literature was retrieved on the Web of Science database from January 2015 to May 2022, and some papers were selected for review from mainstream journals and conferences, such as IEEE Transactions on Medical Imaging, Medical Image Analysis, Physics in Medicine and Biology, Medical Physics, MICCAI, etc. Among them, 65 references are on automatic segmentation, 15 references on automatic landmark detection, and eight references on automatic registration. In the elaboration of the review, first, an overview of deep learning in MIC is presented. Then, the application of deep learning methods is systematically summarized according to the clinical needs, and generalized into segmentation, landmark detection and registration of head and neck medical images. In segmentation, it is mainly focused on the automatic segmentation of high-risk organs, head and neck tumors, skull structure and teeth, including the analysis of their advantages, differences and shortcomings. In landmark detection, the focus is mainly on the introduction of landmark detection in cephalometric and craniomaxillofacial images, and the analysis of their advantages and disadvantages. In registration, deep learning networks for multimodal image registration of the head and neck are presented. Finally, their shortcomings and future development directions are systematically discussed. The study aims to serve as a reference and guidance for researchers, engineers or doctors engaged in medical image analysis of head and neck surgery.
Collapse
|
11
|
Wang J, Chen Z, Yang C, Qu B, Ma L, Fan W, Zhou Q, Zheng Q, Xu S. Evaluation Exploration of Atlas-Based and Deep Learning-Based Automatic Contouring for Nasopharyngeal Carcinoma. Front Oncol 2022; 12:833816. [PMID: 35433460 PMCID: PMC9008357 DOI: 10.3389/fonc.2022.833816] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 02/25/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose The purpose of this study was to evaluate and explore the difference between an atlas-based and deep learning (DL)-based auto-segmentation scheme for organs at risk (OARs) of nasopharyngeal carcinoma cases to provide valuable help for clinical practice. Methods 120 nasopharyngeal carcinoma cases were established in the MIM Maestro (atlas) database and trained by a DL-based model (AccuContour®), and another 20 nasopharyngeal carcinoma cases were randomly selected outside the atlas database. The experienced physicians contoured 14 OARs from 20 patients based on the published consensus guidelines, and these were defined as the reference volumes (Vref). Meanwhile, these OARs were auto-contoured using an atlas-based model, a pre-built DL-based model, and an on-site trained DL-based model. These volumes were named Vatlas, VDL-pre-built, and VDL-trained, respectively. The similarities between Vatlas, VDL-pre-built, VDL-trained, and Vref were assessed using the Dice similarity coefficient (DSC), Jaccard coefficient (JAC), maximum Hausdorff distance (HDmax), and deviation of centroid (DC) methods. A one-way ANOVA test was carried out to show the differences (between each two of them). Results The results of the three methods were almost similar for the brainstem and eyes. For inner ears and temporomandibular joints, the results of the pre-built DL-based model are the worst, as well as the results of atlas-based auto-segmentation for the lens. For the segmentation of optic nerves, the trained DL-based model shows the best performance (p < 0.05). For the contouring of the oral cavity, the DSC value of VDL-pre-built is the smallest, and VDL-trained is the most significant (p < 0.05). For the parotid glands, the DSC of Vatlas is the minimum (about 0.80 or so), and VDL-pre-built and VDL-trained are slightly larger (about 0.82 or so). In addition to the oral cavity, parotid glands, and the brainstem, the maximum Hausdorff distances of the other organs are below 0.5 cm using the trained DL-based segmentation model. The trained DL-based segmentation method behaves well in the contouring of all the organs that the maximum average deviation of the centroid is no more than 0.3 cm. Conclusion The trained DL-based segmentation performs significantly better than atlas-based segmentation for nasopharyngeal carcinoma, especially for the OARs with small volumes. Although some delineation results still need further modification, auto-segmentation methods improve the work efficiency and provide a level of help for clinical work.
Collapse
Affiliation(s)
- Jinyuan Wang
- Department of Radiation Oncology, The First Medical Center of the Chinese PLA General Hospital, Beijing, China
| | | | | | - Baolin Qu
- Department of Radiation Oncology, The First Medical Center of the Chinese PLA General Hospital, Beijing, China
| | - Lin Ma
- Department of Radiation Oncology, The First Medical Center of the Chinese PLA General Hospital, Beijing, China
| | - Wenjun Fan
- Department of Radiation Oncology, The First Medical Center of the Chinese PLA General Hospital, Beijing, China
| | - Qichao Zhou
- Manteia Technologies Co., Ltd., Xiamen, China
| | - Qingzeng Zheng
- Department of Radiation Oncology, Beijing Geriatric Hospital, Beijing, China
| | - Shouping Xu
- Department of Radiation Oncology, The First Medical Center of the Chinese PLA General Hospital, Beijing, China
| |
Collapse
|
12
|
Henderson EG, Vasquez Osorio EM, van Herk M, Green AF. Optimising a 3D convolutional neural network for head and neck computed tomography segmentation with limited training data. Phys Imaging Radiat Oncol 2022; 22:44-50. [PMID: 35514528 PMCID: PMC9065428 DOI: 10.1016/j.phro.2022.04.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 04/11/2022] [Accepted: 04/20/2022] [Indexed: 11/19/2022] Open
Abstract
Convolutional neural networks (CNNs) are used for auto-segmentation in radiotherapy. However, CNNs rely on large, high-quality datasets: a scarcity in radiotherapy. We develop a CNN model, trained with limited data, for accurate segmentation. Multiple experiments were performed to optimise key features of our custom model. Our model is competitive with state-of-the-art methods on a public dataset.
Background and purpose Convolutional neural networks (CNNs) are increasingly used to automate segmentation for radiotherapy planning, where accurate segmentation of organs-at-risk (OARs) is crucial. Training CNNs often requires large amounts of data. However, large, high quality datasets are scarce. The aim of this study was to develop a CNN capable of accurate head and neck (HN) 3D auto-segmentation of planning CT scans using a small training dataset (34 CTs). Materials and Method Elements of our custom CNN architecture were varied to optimise segmentation performance. We tested and evaluated the impact of: using multiple contrast channels for the CT scan input at specific soft tissue and bony anatomy windows, resize vs. transpose convolutions, and loss functions based on overlap metrics and cross-entropy in different combinations. Model segmentation performance was compared with the inter-observer deviation of two doctors’ gold standard segmentations using the 95th percentile Hausdorff distance and mean distance-to-agreement (mDTA). The best performing configuration was further validated on a popular public dataset to compare with state-of-the-art (SOTA) auto-segmentation methods. Results Our best performing CNN configuration was competitive with current SOTA methods when evaluated on the public dataset with mDTA of (0.81±0.31) mm for the brainstem, (0.20±0.08) mm for the mandible, (0.77±0.14) mm for the left parotid and (0.81±0.28) mm for the right parotid. Conclusions Through careful tuning and customisation we trained a 3D CNN with a small dataset to produce segmentations of HN OARs with an accuracy that is comparable with inter-clinician deviations. Our proposed model performed competitively with current SOTA methods.
Collapse
Affiliation(s)
- Edward G.A. Henderson
- The University of Manchester, Oxford Rd, Manchester M13 9PL, UK
- Corresponding author.
| | - Eliana M. Vasquez Osorio
- The University of Manchester, Oxford Rd, Manchester M13 9PL, UK
- Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester M20 4BX, UK
| | - Marcel van Herk
- The University of Manchester, Oxford Rd, Manchester M13 9PL, UK
- Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester M20 4BX, UK
| | - Andrew F. Green
- The University of Manchester, Oxford Rd, Manchester M13 9PL, UK
- Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester M20 4BX, UK
| |
Collapse
|