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Genkin D, Zanette B, Grzela P, Benkert T, Subbarao P, Moraes TJ, Katz S, Ratjen F, Santyr G, Kirby M. Semiautomated Segmentation and Analysis of Airway Lumen in Pediatric Patients Using Ultra Short Echo Time MRI. Acad Radiol 2024; 31:648-659. [PMID: 37550154 DOI: 10.1016/j.acra.2023.07.009] [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: 05/23/2023] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 08/09/2023]
Abstract
RATIONALE AND OBJECTIVES Ultra short echo time (UTE) magnetic resonance imaging (MRI) pulse sequences have shown promise for airway assessment, but the feasibility and repeatability in the pediatric lung are unknown. The purpose of this work was to develop a semiautomated UTE MRI airway segmentation pipeline from the trachea-to-tertiary airways in pediatric participants and assess repeatability and lumen diameter correlations to lung function. MATERIALS AND METHODS A total of 29 participants (n = 7 healthy, n = 11 cystic fibrosis, n = 6 asthma, and n = 5 ex-preterm), aged 7-18 years, were imaged using a 3D stack-of-spirals UTE examination at 3 T. Two independent observers performed airway segmentations using a pipeline developed in-house; observer 1 repeated segmentations 1 month later. Segmentations were extracted using region-growing with leak detection, then manually edited if required. The airway trees were skeletonized, pruned, and labeled. Airway lumen diameter measurements were extracted using ray casting. Intra- and interobserver variability was assessed using the Sørensen-Dice coefficient (DSC) and intra-class correlation coefficient (ICC). Correlations between lumen diameter and pulmonary function were assessed using Spearman's correlation coefficient. RESULTS For airway segmentations and lumen diameter, intra- and interobserver DSCs were 0.88 and 0.80, while ICCs were 0.95 and 0.89, respectively. The variability increased from the trachea-to-tertiary airways for intra- (DSC: 0.91-0.64; ICC: 0.91-0.49) and interobserver (DSC: 0.84-0.51; ICC: 0.89-0.21) measurements. Lumen diameter was significantly correlated with forced expiratory volume in 1 second and forced vital capacity (P < .05). CONCLUSION UTE MRI airway segmentation from the trachea-to-tertiary airways in pediatric participants across a range of diseases is feasible. The UTE MRI-derived lumen measurements were repeatable and correlated with lung function.
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Affiliation(s)
- Daniel Genkin
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada (D.G.)
| | - Brandon Zanette
- Program in Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada (B.Z., P.G., P.S., T.J.M., F.R., G.S.)
| | - Patrick Grzela
- Program in Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada (B.Z., P.G., P.S., T.J.M., F.R., G.S.)
| | - Thomas Benkert
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany (T.B.)
| | - Padmaja Subbarao
- Program in Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada (B.Z., P.G., P.S., T.J.M., F.R., G.S.); Department of Pediatrics, University of Toronto, Toronto, ON, Canada (P.S., T.J.M., F.R.)
| | - Theo J Moraes
- Program in Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada (B.Z., P.G., P.S., T.J.M., F.R., G.S.); Department of Pediatrics, University of Toronto, Toronto, ON, Canada (P.S., T.J.M., F.R.)
| | - Sherri Katz
- Department of Pediatrics, University of Ottawa, Ottawa, ON, Canada (S.K.); Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada (S.K.)
| | - Felix Ratjen
- Program in Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada (B.Z., P.G., P.S., T.J.M., F.R., G.S.); Department of Pediatrics, University of Toronto, Toronto, ON, Canada (P.S., T.J.M., F.R.)
| | - Giles Santyr
- Program in Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada (B.Z., P.G., P.S., T.J.M., F.R., G.S.); Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada (G.S.)
| | - Miranda Kirby
- Department of Physics, Toronto Metropolitan University, Kerr Hall South Bldg., Room KHS-344, 350 Victoria St., Toronto, ON M5B 2K3, Canada (M.K.).
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Zhang M, Wu Y, Zhang H, Qin Y, Zheng H, Tang W, Arnold C, Pei C, Yu P, Nan Y, Yang G, Walsh S, Marshall DC, Komorowski M, Wang P, Guo D, Jin D, Wu Y, Zhao S, Chang R, Zhang B, Lu X, Qayyum A, Mazher M, Su Q, Wu Y, Liu Y, Zhu Y, Yang J, Pakzad A, Rangelov B, Estepar RSJ, Espinosa CC, Sun J, Yang GZ, Gu Y. Multi-site, Multi-domain Airway Tree Modeling. Med Image Anal 2023; 90:102957. [PMID: 37716199 DOI: 10.1016/j.media.2023.102957] [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: 03/27/2023] [Revised: 06/07/2023] [Accepted: 09/04/2023] [Indexed: 09/18/2023]
Abstract
Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to the quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and extensive clinical efforts for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Both quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage (https://atm22.grand-challenge.org/).
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Affiliation(s)
- Minghui Zhang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yangqian Wu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Hanxiao Zhang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yulei Qin
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Hao Zheng
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Wen Tang
- InferVision Medical Technology Co., Ltd., Beijing, China
| | | | - Chenhao Pei
- InferVision Medical Technology Co., Ltd., Beijing, China
| | - Pengxin Yu
- InferVision Medical Technology Co., Ltd., Beijing, China
| | - Yang Nan
- Imperial College London, London, UK
| | | | | | | | | | - Puyang Wang
- Alibaba DAMO Academy, 969 West Wen Yi Road, Hangzhou, Zhejiang, China
| | - Dazhou Guo
- Alibaba DAMO Academy USA, 860 Washington Street, 8F, NY, USA
| | - Dakai Jin
- Alibaba DAMO Academy USA, 860 Washington Street, 8F, NY, USA
| | - Ya'nan Wu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shuiqing Zhao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Runsheng Chang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Boyu Zhang
- A.I R&D Center, Sanmed Biotech Inc., No. 266 Tongchang Road, Xiangzhou District, Zhuhai, Guangdong, China
| | - Xing Lu
- A.I R&D Center, Sanmed Biotech Inc., T220 Trade st. SanDiego, CA, USA
| | - Abdul Qayyum
- ENIB, UMR CNRS 6285 LabSTICC, Brest, 29238, France
| | - Moona Mazher
- Department of Computer Engineering and Mathematics, University Rovira I Virgili, Tarragona, Spain
| | - Qi Su
- Shanghai Jiao Tong University, Shanghai, China
| | - Yonghuang Wu
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Ying'ao Liu
- University of Science and Technology of China, Hefei, Anhui, China
| | | | - Jiancheng Yang
- Dianei Technology, Shanghai, China; EPFL, Lausanne, Switzerland
| | - Ashkan Pakzad
- Medical Physics and Biomedical Engineering Department, University College London, London, UK
| | - Bojidar Rangelov
- Center for Medical Image Computing, University College London, London, UK
| | | | | | - Jiayuan Sun
- Department of Respiratory and Critical Care Medicine, Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai, China.
| | - Guang-Zhong Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Yun Gu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China.
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Saha PK, Nadeem SA, Comellas AP. A Survey on Artificial Intelligence in Pulmonary Imaging. WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY 2023; 13:e1510. [PMID: 38249785 PMCID: PMC10796150 DOI: 10.1002/widm.1510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 06/21/2023] [Indexed: 01/23/2024]
Abstract
Over the last decade, deep learning (DL) has contributed a paradigm shift in computer vision and image recognition creating widespread opportunities of using artificial intelligence in research as well as industrial applications. DL has been extensively studied in medical imaging applications, including those related to pulmonary diseases. Chronic obstructive pulmonary disease, asthma, lung cancer, pneumonia, and, more recently, COVID-19 are common lung diseases affecting nearly 7.4% of world population. Pulmonary imaging has been widely investigated toward improving our understanding of disease etiologies and early diagnosis and assessment of disease progression and clinical outcomes. DL has been broadly applied to solve various pulmonary image processing challenges including classification, recognition, registration, and segmentation. This paper presents a survey of pulmonary diseases, roles of imaging in translational and clinical pulmonary research, and applications of different DL architectures and methods in pulmonary imaging with emphasis on DL-based segmentation of major pulmonary anatomies such as lung volumes, lung lobes, pulmonary vessels, and airways as well as thoracic musculoskeletal anatomies related to pulmonary diseases.
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Affiliation(s)
- Punam K Saha
- Departments of Radiology and Electrical and Computer Engineering, University of Iowa, Iowa City, IA, 52242
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Dudurych I, Garcia-Uceda A, Petersen J, Du Y, Vliegenthart R, de Bruijne M. Reproducibility of a combined artificial intelligence and optimal-surface graph-cut method to automate bronchial parameter extraction. Eur Radiol 2023; 33:6718-6725. [PMID: 37071168 PMCID: PMC10511366 DOI: 10.1007/s00330-023-09615-y] [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: 07/26/2022] [Revised: 03/02/2023] [Accepted: 03/14/2023] [Indexed: 04/19/2023]
Abstract
OBJECTIVES Computed tomography (CT)-based bronchial parameters correlate with disease status. Segmentation and measurement of the bronchial lumen and walls usually require significant manpower. We evaluate the reproducibility of a deep learning and optimal-surface graph-cut method to automatically segment the airway lumen and wall, and calculate bronchial parameters. METHODS A deep-learning airway segmentation model was newly trained on 24 Imaging in Lifelines (ImaLife) low-dose chest CT scans. This model was combined with an optimal-surface graph-cut for airway wall segmentation. These tools were used to calculate bronchial parameters in CT scans of 188 ImaLife participants with two scans an average of 3 months apart. Bronchial parameters were compared for reproducibility assessment, assuming no change between scans. RESULTS Of 376 CT scans, 374 (99%) were successfully measured. Segmented airway trees contained a mean of 10 generations and 250 branches. The coefficient of determination (R2) for the luminal area (LA) ranged from 0.93 at the trachea to 0.68 at the 6th generation, decreasing to 0.51 at the 8th generation. Corresponding values for Wall Area Percentage (WAP) were 0.86, 0.67, and 0.42, respectively. Bland-Altman analysis of LA and WAP per generation demonstrated mean differences close to 0; limits of agreement (LoA) were narrow for WAP and Pi10 (± 3.7% of mean) and wider for LA (± 16.4-22.8% for 2-6th generations). From the 7th generation onwards, there was a sharp decrease in reproducibility and a widening LoA. CONCLUSION The outlined approach for automatic bronchial parameter measurement on low-dose chest CT scans is a reliable way to assess the airway tree down to the 6th generation. STATEMENT ON CLINICAL RELEVANCE This reliable and fully automatic pipeline for bronchial parameter measurement on low-dose CT scans has potential applications in screening for early disease and clinical tasks such as virtual bronchoscopy or surgical planning, while also enabling the exploration of bronchial parameters in large datasets. KEY POINTS • Deep learning combined with optimal-surface graph-cut provides accurate airway lumen and wall segmentations on low-dose CT scans. • Analysis of repeat scans showed that the automated tools had moderate-to-good reproducibility of bronchial measurements down to the 6th generation airway. • Automated measurement of bronchial parameters enables the assessment of large datasets with less man-hours.
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Affiliation(s)
- Ivan Dudurych
- Department of Radiology, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
| | - Antonio Garcia-Uceda
- Department of Radiology and Nuclear Medicine, Erasmus MC, BIGR - Na 26-20, Doctor Molewaterplein 40, 3015 GD, Rotterdam, Netherlands
- Department of Paediatric Pulmonology and Allergology, Erasmus MC-Sophia Children Hospital, Rotterdam, Netherlands
| | - Jens Petersen
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Yihui Du
- Department of Epidemiology, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
- Data Science in Health (DASH), University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
| | - Marleen de Bruijne
- Department of Radiology and Nuclear Medicine, Erasmus MC, BIGR - Na 26-20, Doctor Molewaterplein 40, 3015 GD, Rotterdam, Netherlands.
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
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Wu Y, Zhao S, Qi S, Feng J, Pang H, Chang R, Bai L, Li M, Xia S, Qian W, Ren H. Two-stage contextual transformer-based convolutional neural network for airway extraction from CT images. Artif Intell Med 2023; 143:102637. [PMID: 37673569 DOI: 10.1016/j.artmed.2023.102637] [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: 01/08/2023] [Revised: 06/14/2023] [Accepted: 08/11/2023] [Indexed: 09/08/2023]
Abstract
Accurate airway segmentation from computed tomography (CT) images is critical for planning navigation bronchoscopy and realizing a quantitative assessment of airway-related chronic obstructive pulmonary disease (COPD). Existing methods face difficulty in airway segmentation, particularly for the small branches of the airway. These difficulties arise due to the constraints of limited labeling and failure to meet clinical use requirements in COPD. We propose a two-stage framework with a novel 3D contextual transformer for segmenting the overall airway and small airway branches using CT images. The method consists of two training stages sharing the same modified 3D U-Net network. The novel 3D contextual transformer block is integrated into both the encoder and decoder path of the network to effectively capture contextual and long-range information. In the first training stage, the proposed network segments the overall airway with the overall airway mask. To improve the performance of the segmentation result, we generate the intrapulmonary airway branch label, and train the network to focus on producing small airway branches in the second training stage. Extensive experiments were performed on in-house and multiple public datasets. Quantitative and qualitative analyses demonstrate that our proposed method extracts significantly more branches and longer lengths of the airway tree while accomplishing state-of-the-art airway segmentation performance. The code is available at https://github.com/zhaozsq/airway_segmentation.
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Affiliation(s)
- Yanan Wu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China.
| | - Shuiqing Zhao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Jie Feng
- School of Chemical Equipment, Shenyang University of Technology, Liaoyang, China.
| | - Haowen Pang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
| | - Runsheng Chang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
| | - Long Bai
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China.
| | - Mengqi Li
- Department of Respiratory, the Second Affiliated Hospital of Dalian Medical University, Dalian, China.
| | - Shuyue Xia
- Respiratory Department, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China.
| | - Wei Qian
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
| | - Hongliang Ren
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China.
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Vijayan R, Sheth N, Mekki L, Lu A, Uneri A, Sisniega A, Magaraggia J, Kleinszig G, Vogt S, Thiboutot J, Lee H, Yarmus L, Siewerdsen JH. 3D-2D image registration in the presence of soft-tissue deformation in image-guided transbronchial interventions. Phys Med Biol 2022; 68. [PMID: 36317269 DOI: 10.1088/1361-6560/ac9e3c] [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: 05/31/2022] [Accepted: 10/27/2022] [Indexed: 11/06/2022]
Abstract
Purpose. Target localization in pulmonary interventions (e.g. transbronchial biopsy of a lung nodule) is challenged by deformable motion and may benefit from fluoroscopic overlay of the target to provide accurate guidance. We present and evaluate a 3D-2D image registration method for fluoroscopic overlay in the presence of tissue deformation using a multi-resolution/multi-scale (MRMS) framework with an objective function that drives registration primarily by soft-tissue image gradients.Methods. The MRMS method registers 3D cone-beam CT to 2D fluoroscopy without gating of respiratory phase by coarse-to-fine resampling and global-to-local rescaling about target regions-of-interest. A variation of the gradient orientation (GO) similarity metric (denotedGO') was developed to downweight bone gradients and drive registration via soft-tissue gradients. Performance was evaluated in terms of projection distance error at isocenter (PDEiso). Phantom studies determined nominal algorithm parameters and capture range. Preclinical studies used a freshly deceased, ventilated porcine specimen to evaluate performance in the presence of real tissue deformation and a broad range of 3D-2D image mismatch.Results. Nominal algorithm parameters were identified that provided robust performance over a broad range of motion (0-20 mm), including an adaptive parameter selection technique to accommodate unknown mismatch in respiratory phase. TheGO'metric yielded median PDEiso= 1.2 mm, compared to 6.2 mm for conventionalGO.Preclinical studies with real lung deformation demonstrated median PDEiso= 1.3 mm with MRMS +GO'registration, compared to 2.2 mm with a conventional transform. Runtime was 26 s and can be reduced to 2.5 s given a prior registration within ∼5 mm as initialization.Conclusions. MRMS registration via soft-tissue gradients achieved accurate fluoroscopic overlay in the presence of deformable lung motion. By driving registration via soft-tissue image gradients, the method avoided false local minima presented by bones and was robust to a wide range of motion magnitude.
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Affiliation(s)
- R Vijayan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - N Sheth
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - L Mekki
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - A Lu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - A Uneri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - A Sisniega
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | | | | | - S Vogt
- Siemens Healthineers, Erlangen, Germany
| | - J Thiboutot
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins Medical Institution, Baltimore, MD, United States of America
| | - H Lee
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins Medical Institution, Baltimore, MD, United States of America
| | - L Yarmus
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins Medical Institution, Baltimore, MD, United States of America
| | - J H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.,Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
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Nadeem SA, Comellas AP, Hoffman EA, Saha PK. Airway Detection in COPD at Low-Dose CT Using Deep Learning and Multiparametric Freeze and Grow. Radiol Cardiothorac Imaging 2022; 4:e210311. [PMID: 36601453 PMCID: PMC9806731 DOI: 10.1148/ryct.210311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 09/27/2022] [Accepted: 10/27/2022] [Indexed: 06/17/2023]
Abstract
PURPOSE To present and validate a fully automated airway detection method at low-dose CT in patients with chronic obstructive pulmonary disease (COPD). MATERIALS AND METHODS In this retrospective study, deep learning (DL) and freeze-and-grow (FG) methods were optimized and applied to automatically detect airways at low-dose CT. Four data sets were used: two data sets consisting of matching standard- and low-dose CT scans from the Genetic Epidemiology of COPD (COPDGene) phase II (2014-2017) cohort (n = 2 × 236; mean age ± SD, 70 years ± 9; 123 women); one data set consisting of low-dose CT scans from the COPDGene phase III (2018-2020) cohort (n = 335; mean age ± SD, 73 years ± 8; 173 women); and one data set consisting of low-dose, anonymized CT scans from the 2003 Dutch-Belgian Randomized Lung Cancer Screening trial (n = 55) acquired by using different CT scanners. Performance measures for different methods were computed and compared by using the Wilcoxon signed rank test. RESULTS At low-dose CT, 56 294 of 62 480 (90.1%) airways of the reference total airway count (TAC) and 32 109 of 37 864 (84.8%) airways of the peripheral TAC (TACp), detected at standard-dose CT, were detected. Significant losses (P < .001) of 14 526 of 76 453 (19.0%) airways and 884 of 6908 (12.8%) airways in the TAC and 12 256 of 43 462 (28.2%) airways and 699 of 3882 (18.0%) airways in the TACp were observed, respectively, for the multiprotocol and multiscanner data without retraining. When using the automated low-dose CT method, TAC values of 347, 342, 323, and 266 and TACp values of 205, 202, 289, and 141 were observed for those who have never smoked and participants at Global Initiative for Chronic Obstructive Lung Disease stages 0, 1, and 2, respectively, which were superior to the respective values previously reported for matching groups when using a semiautomated method at standard-dose CT. CONCLUSION A low-cost, automated CT-based airway detection method was suitable for investigation of airway phenotypes at low-dose CT.Keywords: Airway, Airway Count, Airway Detection, Chronic Obstructive Pulmonary Disease, CT, Deep Learning, Generalizability, Low-Dose CT, Segmentation, Thorax, LungClinical trial registration no. NCT00608764 Supplemental material is available for this article. © RSNA, 2022.
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Li H, Tang Z, Nan Y, Yang G. Human treelike tubular structure segmentation: A comprehensive review and future perspectives. Comput Biol Med 2022; 151:106241. [PMID: 36379190 DOI: 10.1016/j.compbiomed.2022.106241] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/16/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.
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Affiliation(s)
- Hao Li
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Zeyu Tang
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Yang Nan
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Guang Yang
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Royal Brompton Hospital, London, United Kingdom.
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Hoffman EA. Origins of and lessons from quantitative functional X-ray computed tomography of the lung. Br J Radiol 2022; 95:20211364. [PMID: 35193364 PMCID: PMC9153696 DOI: 10.1259/bjr.20211364] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/20/2022] [Accepted: 01/27/2022] [Indexed: 12/16/2022] Open
Abstract
Functional CT of the lung has emerged from quantitative CT (qCT). Structural details extracted at multiple lung volumes offer indices of function. Additionally, single volumetric images, if acquired at standardized lung volumes and body posture, can be used to model function by employing such engineering techniques as computational fluid dynamics. With the emergence of multispectral CT imaging including dual energy from energy integrating CT scanners and multienergy binning using the newly released photon counting CT technology, function is tagged via use of contrast agents. Lung disease phenotypes have previously been lumped together by the limitations of spirometry and plethysmography. QCT and its functional embodiment have been imbedded into studies seeking to characterize chronic obstructive pulmonary disease, severe asthma, interstitial lung disease and more. Reductions in radiation dose by an order of magnitude or more have been achieved. At the same time, we have seen significant increases in spatial and density resolution along with methodologic validations of extracted metrics. Together, these have allowed attention to turn towards more mild forms of disease and younger populations. In early applications, clinical CT offered anatomic details of the lung. Functional CT offers regional measures of lung mechanics, the assessment of functional small airways disease, as well as regional ventilation-perfusion matching (V/Q) and more. This paper will focus on the use of quantitative/functional CT for the non-invasive exploration of dynamic three-dimensional functioning of the breathing lung and beating heart within the unique negative pressure intrathoracic environment of the closed chest.
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Affiliation(s)
- Eric A Hoffman
- Departments of Radiology, Internal Medicine and Biomedical Engineering University of Iowa, Iowa, United States
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Wang G, Zhai S, Lasio G, Zhang B, Yi B, Chen S, Macvittie TJ, Metaxas D, Zhou J, Zhang S. Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis From Lung CT Scans With Multi-Scale Guided Dense Attention. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:531-542. [PMID: 34606451 PMCID: PMC9271367 DOI: 10.1109/tmi.2021.3117564] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Computed Tomography (CT) plays an important role in monitoring radiation-induced Pulmonary Fibrosis (PF), where accurate segmentation of the PF lesions is highly desired for diagnosis and treatment follow-up. However, the task is challenged by ambiguous boundary, irregular shape, various position and size of the lesions, as well as the difficulty in acquiring a large set of annotated volumetric images for training. To overcome these problems, we propose a novel convolutional neural network called PF-Net and incorporate it into a semi-supervised learning framework based on Iterative Confidence-based Refinement And Weighting of pseudo Labels (I-CRAWL). Our PF-Net combines 2D and 3D convolutions to deal with CT volumes with large inter-slice spacing, and uses multi-scale guided dense attention to segment complex PF lesions. For semi-supervised learning, our I-CRAWL employs pixel-level uncertainty-based confidence-aware refinement to improve the accuracy of pseudo labels of unannotated images, and uses image-level uncertainty for confidence-based image weighting to suppress low-quality pseudo labels in an iterative training process. Extensive experiments with CT scans of Rhesus Macaques with radiation-induced PF showed that: 1) PF-Net achieved higher segmentation accuracy than existing 2D, 3D and 2.5D neural networks, and 2) I-CRAWL outperformed state-of-the-art semi-supervised learning methods for the PF lesion segmentation task. Our method has a potential to improve the diagnosis of PF and clinical assessment of side effects of radiotherapy for lung cancers.
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Bi Y, Jiang C, Qi H, Zhou H, Sun L. Computed Tomography Image Texture under Feature Extraction Algorithm in the Diagnosis of Effect of Specific Nursing Intervention on Mycoplasma Pneumonia in Children. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6059060. [PMID: 34697567 PMCID: PMC8541873 DOI: 10.1155/2021/6059060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 09/25/2021] [Accepted: 09/27/2021] [Indexed: 11/17/2022]
Abstract
To evaluate the effect of specific nursing intervention in children with mycoplasma pneumonia (MP), a feature extraction algorithm based on gray level co-occurrence matrix (GLCM) was proposed and combined with computed tomography (CT) image texture features. Then, 98 children with MP were rolled into the observation group with 49 cases (specific nursing) and the control group with 49 cases (routine nursing). CT images based on feature extraction algorithm of optimized GLCM were used to examine the children before and after nursing intervention, and the recovery of the two groups of children was discussed. The results showed that the proportion of lung texture increase, rope shadow, ground glass shadow, atelectasis, and pleural effusion in the observation group (24.11%, 3.86%, 8.53%, 15.03%, and 3.74%) was significantly lower than that in the control group (28.53%, 10.23%, 13.34%, 21.15%, and 8.13%) after nursing (P < 0.05). There were no significant differences in the proportion of small patchy shadows, large patchy consolidation shadows, and bronchiectasis between the observation group and the control group (P > 0.05). In the course of nursing intervention, in the observation group, the disappearance time of cough, normal temperature, disappearance time of lung rales, and absorption time of lung shadow (2.15 ± 0.86 days, 4.81 ± 1.14 days, 3.64 ± 0.55 days, and 5.96 ± 0.62 days) were significantly shorter than those in the control group (2.87 ± 0.95 days, 3.95 ± 1.06 days, 4.51 ± 1.02 days, and 8.14 ± 1.35 days) (P < 0.05). After nursing intervention, the proportion of satisfaction and total satisfaction in the experimental group (67.08% and 28.66%) was significantly higher than that in the control group (40.21% and 47.39%), while the proportion of dissatisfaction (4.26%) was significantly lower than that in the control group (12.4%) (P < 0.05). To sum up, specific nursing intervention was more beneficial to improve the progress of characterization recovery and the overall recovery effect of children with MP relative to conventional nursing. CT image based on feature extraction algorithm of optimized GLCM was of good adoption value in the diagnosis and treatment of MP in children.
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Affiliation(s)
- Yuyan Bi
- Department of Pediatric Ward, Jinan City People's Hospital, Jinan 271199, Shandong Province, China
| | - Cuifeng Jiang
- Department of Pediatric Surgery, Jinan City People's Hospital, Jinan 271199, Shandong Province, China
| | - Hua Qi
- Department of Pediatric Ward, Jinan City People's Hospital, Jinan 271199, Shandong Province, China
| | - Haiwei Zhou
- Department of Pediatric Ward, Jinan City People's Hospital, Jinan 271199, Shandong Province, China
| | - Lixia Sun
- Department of Nursing, Jinan City People's Hospital, Jinan 271199, Shandong Province, China
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Garcia-Uceda A, Selvan R, Saghir Z, Tiddens HAWM, de Bruijne M. Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks. Sci Rep 2021; 11:16001. [PMID: 34362949 PMCID: PMC8346579 DOI: 10.1038/s41598-021-95364-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 07/21/2021] [Indexed: 12/11/2022] Open
Abstract
This paper presents a fully automatic and end-to-end optimised airway segmentation method for thoracic computed tomography, based on the U-Net architecture. We use a simple and low-memory 3D U-Net as backbone, which allows the method to process large 3D image patches, often comprising full lungs, in a single pass through the network. This makes the method simple, robust and efficient. We validated the proposed method on three datasets with very different characteristics and various airway abnormalities: (1) a dataset of pediatric patients including subjects with cystic fibrosis, (2) a subset of the Danish Lung Cancer Screening Trial, including subjects with chronic obstructive pulmonary disease, and (3) the EXACT'09 public dataset. We compared our method with other state-of-the-art airway segmentation methods, including relevant learning-based methods in the literature evaluated on the EXACT'09 data. We show that our method can extract highly complete airway trees with few false positive errors, on scans from both healthy and diseased subjects, and also that the method generalizes well across different datasets. On the EXACT'09 test set, our method achieved the second highest sensitivity score among all methods that reported good specificity.
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Affiliation(s)
- Antonio Garcia-Uceda
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 CE, Rotterdam, The Netherlands.
- Department of Pediatric Pulmonology and Allergology, Erasmus MC-Sophia Children Hospital, 3015 CE, Rotterdam, The Netherlands.
| | - Raghavendra Selvan
- Department of Computer Science, University of Copenhagen, 2100, Copenhagen, Denmark
- Department of Neuroscience, University of Copenhagen, 2200, Copenhagen, Denmark
| | - Zaigham Saghir
- Department of Medicine, Section of Pulmonary Medicine, Herlev-Gentofte Hospital, Copenhagen University Hospital, 2900, Hellerup, Denmark
| | - Harm A W M Tiddens
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 CE, Rotterdam, The Netherlands
- Department of Pediatric Pulmonology and Allergology, Erasmus MC-Sophia Children Hospital, 3015 CE, Rotterdam, The Netherlands
| | - Marleen de Bruijne
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 CE, Rotterdam, The Netherlands.
- Department of Computer Science, University of Copenhagen, 2100, Copenhagen, Denmark.
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