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Garg Y, Seetharam K, Sharma M, Rohita DK, Nabi W. Role of Deep Learning in Computed Tomography. Cureus 2023; 15:e39160. [PMID: 37332431 PMCID: PMC10275744 DOI: 10.7759/cureus.39160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/17/2023] [Indexed: 06/20/2023] Open
Abstract
Computed tomography has played an instrumental role in the understanding of the pathophysiology of atherosclerosis in coronary artery disease. It enables visualization of the plaque obstruction and vessel stenosis in a comprehensive manner. As technology for computed tomography is constantly evolving, coronary applications and possibilities are constantly expanding. This influx of information can overwhelm a physician's ability to interpret information in this era of big data. Machine learning is a revolutionary approach that can help provide limitless pathways in patient management. Within these machine algorithms, deep learning has tremendous potential and can revolutionize computed tomography and cardiovascular imaging. In this review article, we highlight the role of deep learning in various aspects of computed tomography.
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Affiliation(s)
- Yash Garg
- Internal Medicine, Wyckoff Heights Medical Center, New York, USA
| | | | - Manjari Sharma
- Internal Medicine, Wyckoff Heights Medical Center, New York, USA
| | - Dipesh K Rohita
- Internal Medicine, Wyckoff Heights Medical Center, New York, USA
| | - Waseem Nabi
- Internal Medicine, Wyckoff Heights Medical Center, New York, USA
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Wang X, Jemaa S, Fredrickson J, Coimbra AF, Nielsen T, De Crespigny A, Bengtsson T, Carano RAD. Heart and bladder detection and segmentation on FDG PET/CT by deep learning. BMC Med Imaging 2022; 22:58. [PMID: 35354384 PMCID: PMC8977865 DOI: 10.1186/s12880-022-00785-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 03/22/2022] [Indexed: 12/04/2022] Open
Abstract
Purpose Positron emission tomography (PET)/ computed tomography (CT) has been extensively used to quantify metabolically active tumors in various oncology indications. However, FDG-PET/CT often encounters false positives in tumor detection due to 18fluorodeoxyglucose (FDG) accumulation from the heart and bladder that often exhibit similar FDG uptake as tumors. Thus, it is necessary to eliminate this source of physiological noise. Major challenges for this task include: (1) large inter-patient variability in the appearance for the heart and bladder. (2) The size and shape of bladder or heart may appear different on PET and CT. (3) Tumors can be very close or connected to the heart or bladder. Approach A deep learning based approach is proposed to segment the heart and bladder on whole body PET/CT automatically. Two 3D U-Nets were developed separately to segment the heart and bladder, where each network receives the PET and CT as a multi-modal input. Data sets were obtained from retrospective clinical trials and include 575 PET/CT for heart segmentation and 538 for bladder segmentation. Results The models were evaluated on a test set from an independent trial and achieved a Dice Similarity Coefficient (DSC) of 0.96 for heart segmentation and 0.95 for bladder segmentation, Average Surface Distance (ASD) of 0.44 mm on heart and 0.90 mm on bladder. Conclusions This methodology could be a valuable component to the FDG-PET/CT data processing chain by removing FDG physiological noise associated with heart and/or bladder accumulation prior to image analysis by manual, semi- or automated tumor analysis methods.
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Shi T, Shahedi M, Caughlin K, Dormer JD, Ma L, Fei B. Semi-automated three-dimensional segmentation for cardiac CT images using deep learning and randomly distributed points. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12034:120341W. [PMID: 36793655 PMCID: PMC9928521 DOI: 10.1117/12.2611594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Given the prevalence of cardiovascular diseases (CVDs), the segmentation of the heart on cardiac computed tomography (CT) remains of great importance. Manual segmentation is time-consuming and intra-and inter-observer variabilities yield inconsistent and inaccurate results. Computer-assisted, and in particular, deep learning approaches to segmentation continue to potentially offer an accurate, efficient alternative to manual segmentation. However, fully automated methods for cardiac segmentation have yet to achieve accurate enough results to compete with expert segmentation. Thus, we focus on a semi-automated deep learning approach to cardiac segmentation that bridges the divide between a higher accuracy from manual segmentation and higher efficiency from fully automated methods. In this approach, we selected a fixed number of points along the surface of the cardiac region to mimic user interaction. Points-distance maps were then generated from these points selections, and a three-dimensional (3D) fully convolutional neural network (FCNN) was trained using points-distance maps to provide a segmentation prediction. Testing our method with different numbers of selected points, we achieved a Dice score from 0.742 to 0.917 across the four chambers. Specifically. Dice scores averaged 0.846 ± 0.059, 0.857 ± 0.052, 0.826 ± 0.062, and 0.824 ± 0.062 for the left atrium, left ventricle, right atrium, and right ventricle, respectively across all points selections. This point-guided, image-independent, deep learning segmentation approach illustrated a promising performance for chamber-by-chamber delineation of the heart in CT images.
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Affiliation(s)
- Ted Shi
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
| | - Maysam Shahedi
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
- Center for Imaging and Surgical Innovation, The University of Texas at Dallas, Richardson, TX
| | - Kayla Caughlin
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
| | - James D. Dormer
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
- Center for Imaging and Surgical Innovation, The University of Texas at Dallas, Richardson, TX
| | - Ling Ma
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
- Center for Imaging and Surgical Innovation, The University of Texas at Dallas, Richardson, TX
| | - Baowei Fei
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
- Center for Imaging and Surgical Innovation, The University of Texas at Dallas, Richardson, TX
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX
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4
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Abdulkareem M, Brahier MS, Zou F, Taylor A, Thomaides A, Bergquist PJ, Srichai MB, Lee AM, Vargas JD, Petersen SE. Generalizable Framework for Atrial Volume Estimation for Cardiac CT Images Using Deep Learning With Quality Control Assessment. Front Cardiovasc Med 2022; 9:822269. [PMID: 35155637 PMCID: PMC8831539 DOI: 10.3389/fcvm.2022.822269] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 01/04/2022] [Indexed: 12/28/2022] Open
Abstract
Objectives Cardiac computed tomography (CCT) is a common pre-operative imaging modality to evaluate pulmonary vein anatomy and left atrial appendage thrombus in patients undergoing catheter ablation (CA) for atrial fibrillation (AF). These images also allow for full volumetric left atrium (LA) measurement for recurrence risk stratification, as larger LA volume (LAV) is associated with higher recurrence rates. Our objective is to apply deep learning (DL) techniques to fully automate the computation of LAV and assess the quality of the computed LAV values. Methods Using a dataset of 85,477 CCT images from 337 patients, we proposed a framework that consists of several processes that perform a combination of tasks including the selection of images with LA from all other images using a ResNet50 classification model, the segmentation of images with LA using a UNet image segmentation model, the assessment of the quality of the image segmentation task, the estimation of LAV, and quality control (QC) assessment. Results Overall, the proposed LAV estimation framework achieved accuracies of 98% (precision, recall, and F1 score metrics) in the image classification task, 88.5% (mean dice score) in the image segmentation task, 82% (mean dice score) in the segmentation quality prediction task, and R2 (the coefficient of determination) value of 0.968 in the volume estimation task. It correctly identified 9 out of 10 poor LAV estimations from a total of 337 patients as poor-quality estimates. Conclusions We proposed a generalizable framework that consists of DL models and computational methods for LAV estimation. The framework provides an efficient and robust strategy for QC assessment of the accuracy for DL-based image segmentation and volume estimation tasks, allowing high-throughput extraction of reproducible LAV measurements to be possible.
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Affiliation(s)
- Musa Abdulkareem
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
- National Institute for Health Research Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- *Correspondence: Musa Abdulkareem
| | - Mark S. Brahier
- Georgetown University School of Medicine, Washington, DC, United States
| | - Fengwei Zou
- Montefiore Medical Centre, Bronx, NY, United States
| | | | | | | | | | - Aaron M. Lee
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
- National Institute for Health Research Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Jose D. Vargas
- Veterans Affairs Medical Center, Washington, DC, United States
- Georgetown University, Washington, DC, United States
| | - Steffen E. Petersen
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
- National Institute for Health Research Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- The Alan Turing Institute, London, United Kingdom
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5
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Aoyama G, Zhao L, Zhao S, Xue X, Zhong Y, Yamauchi H, Tsukihara H, Maeda E, Ino K, Tomii N, Takagi S, Sakuma I, Ono M, Sakaguchi T. Automatic Aortic Valve Cusps Segmentation from CT Images Based on the Cascading Multiple Deep Neural Networks. J Imaging 2022; 8:11. [PMID: 35049852 PMCID: PMC8780687 DOI: 10.3390/jimaging8010011] [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: 11/25/2021] [Revised: 12/22/2021] [Accepted: 12/30/2021] [Indexed: 02/01/2023] Open
Abstract
Accurate morphological information on aortic valve cusps is critical in treatment planning. Image segmentation is necessary to acquire this information, but manual segmentation is tedious and time consuming. In this paper, we propose a fully automatic aortic valve cusps segmentation method from CT images by combining two deep neural networks, spatial configuration-Net for detecting anatomical landmarks and U-Net for segmentation of aortic valve components. A total of 258 CT volumes of end systolic and end diastolic phases, which include cases with and without severe calcifications, were collected and manually annotated for each aortic valve component. The collected CT volumes were split 6:2:2 for the training, validation and test steps, and our method was evaluated by five-fold cross validation. The segmentation was successful for all CT volumes with 69.26 s as mean processing time. For the segmentation results of the aortic root, the right-coronary cusp, the left-coronary cusp and the non-coronary cusp, mean Dice Coefficient were 0.95, 0.70, 0.69, and 0.67, respectively. There were strong correlations between measurement values automatically calculated based on the annotations and those based on the segmentation results. The results suggest that our method can be used to automatically obtain measurement values for aortic valve morphology.
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Affiliation(s)
- Gakuto Aoyama
- Research and Development Center, Canon Medical Systems Corporation, 1385 Shimoishigami, Otawara 324-8550, Japan;
| | - Longfei Zhao
- Research and Development Center, Canon Medical Systems (CHINA) CO., LTD., Chao Yang District, Beijing 100015, China; (L.Z.); (S.Z.); (X.X.); (Y.Z.)
| | - Shun Zhao
- Research and Development Center, Canon Medical Systems (CHINA) CO., LTD., Chao Yang District, Beijing 100015, China; (L.Z.); (S.Z.); (X.X.); (Y.Z.)
| | - Xiao Xue
- Research and Development Center, Canon Medical Systems (CHINA) CO., LTD., Chao Yang District, Beijing 100015, China; (L.Z.); (S.Z.); (X.X.); (Y.Z.)
| | - Yunxin Zhong
- Research and Development Center, Canon Medical Systems (CHINA) CO., LTD., Chao Yang District, Beijing 100015, China; (L.Z.); (S.Z.); (X.X.); (Y.Z.)
| | - Haruo Yamauchi
- The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan; (H.Y.); (H.T.); (E.M.); (K.I.); (M.O.)
| | - Hiroyuki Tsukihara
- The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan; (H.Y.); (H.T.); (E.M.); (K.I.); (M.O.)
- School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan; (N.T.); (S.T.); (I.S.)
| | - Eriko Maeda
- The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan; (H.Y.); (H.T.); (E.M.); (K.I.); (M.O.)
| | - Kenji Ino
- The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan; (H.Y.); (H.T.); (E.M.); (K.I.); (M.O.)
| | - Naoki Tomii
- School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan; (N.T.); (S.T.); (I.S.)
| | - Shu Takagi
- School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan; (N.T.); (S.T.); (I.S.)
| | - Ichiro Sakuma
- School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan; (N.T.); (S.T.); (I.S.)
| | - Minoru Ono
- The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan; (H.Y.); (H.T.); (E.M.); (K.I.); (M.O.)
| | - Takuya Sakaguchi
- Research and Development Center, Canon Medical Systems Corporation, 1385 Shimoishigami, Otawara 324-8550, Japan;
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Samarasinghe G, Jameson M, Vinod S, Field M, Dowling J, Sowmya A, Holloway L. Deep learning for segmentation in radiation therapy planning: a review. J Med Imaging Radiat Oncol 2021; 65:578-595. [PMID: 34313006 DOI: 10.1111/1754-9485.13286] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 06/29/2021] [Indexed: 12/21/2022]
Abstract
Segmentation of organs and structures, as either targets or organs-at-risk, has a significant influence on the success of radiation therapy. Manual segmentation is a tedious and time-consuming task for clinicians, and inter-observer variability can affect the outcomes of radiation therapy. The recent hype over deep neural networks has added many powerful auto-segmentation methods as variations of convolutional neural networks (CNN). This paper presents a descriptive review of the literature on deep learning techniques for segmentation in radiation therapy planning. The most common CNN architecture across the four clinical sub sites considered was U-net, with the majority of deep learning segmentation articles focussed on head and neck normal tissue structures. The most common data sets were CT images from an inhouse source, along with some public data sets. N-fold cross-validation was commonly employed; however, not all work separated training, test and validation data sets. This area of research is expanding rapidly. To facilitate comparisons of proposed methods and benchmarking, consistent use of appropriate metrics and independent validation should be carefully considered.
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Affiliation(s)
- Gihan Samarasinghe
- School of Computer Science and Engineering, University of New South Wales, Sydney, New South Wales, Australia.,Ingham Institute for Applied Medical Research and South Western Sydney Clinical School, UNSW, Liverpool, New South Wales, Australia
| | - Michael Jameson
- Genesiscare, Sydney, New South Wales, Australia.,St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Shalini Vinod
- Ingham Institute for Applied Medical Research and South Western Sydney Clinical School, UNSW, Liverpool, New South Wales, Australia.,Liverpool Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
| | - Matthew Field
- Ingham Institute for Applied Medical Research and South Western Sydney Clinical School, UNSW, Liverpool, New South Wales, Australia.,Liverpool Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
| | - Jason Dowling
- Commonwealth Scientific and Industrial Research Organisation, Australian E-Health Research Centre, Herston, Queensland, Australia
| | - Arcot Sowmya
- School of Computer Science and Engineering, University of New South Wales, Sydney, New South Wales, Australia
| | - Lois Holloway
- Ingham Institute for Applied Medical Research and South Western Sydney Clinical School, UNSW, Liverpool, New South Wales, Australia.,Liverpool Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
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Nemoto T, Futakami N, Kunieda E, Yagi M, Takeda A, Akiba T, Mutu E, Shigematsu N. Effects of sample size and data augmentation on U-Net-based automatic segmentation of various organs. Radiol Phys Technol 2021; 14:318-327. [PMID: 34254251 DOI: 10.1007/s12194-021-00630-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 07/01/2021] [Accepted: 07/05/2021] [Indexed: 10/20/2022]
Abstract
Deep learning has demonstrated high efficacy for automatic segmentation in contour delineation, which is crucial in radiation therapy planning. However, the collection, labeling, and management of medical imaging data can be challenging. This study aims to elucidate the effects of sample size and data augmentation on the automatic segmentation of computed tomography images using U-Net, a deep learning method. For the chest and pelvic regions, 232 and 556 cases are evaluated, respectively. We investigate multiple conditions by changing the sum of the training and validation datasets across a broad range of values: 10-200 and 10-500 cases for the chest and pelvic regions, respectively. A U-Net is constructed, and horizontal-flip data augmentation, which produces left and right inverse images resulting in twice the number of images, is compared with no augmentation for each training session. All lung cases and more than 100 prostate, bladder, and rectum cases indicate that adding horizontal-flip data augmentation is almost as effective as doubling the number of cases. The slope of the Dice similarity coefficient (DSC) in all organs decreases rapidly until approximately 100 cases, stabilizes after 200 cases, and shows minimal changes as the number of cases is increased further. The DSCs stabilize at a smaller sample size with the incorporation of data augmentation in all organs except the heart. This finding is applicable to the automation of radiation therapy for rare cancers, where large datasets may be difficult to obtain.
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Affiliation(s)
- Takafumi Nemoto
- Department of Radiology, Keio University School of Medicine, Shinanomachi 35, Shinjuku-ku, Tokyo, 160-8582, Japan.
| | - Natsumi Futakami
- Department of Radiation Oncology, Tokai University School of Medicine, Shimokasuya 143, Isehara-shi, Kanagawa, 259-1143, Japan
| | - Etsuo Kunieda
- Department of Radiology, Keio University School of Medicine, Shinanomachi 35, Shinjuku-ku, Tokyo, 160-8582, Japan.,Department of Radiation Oncology, Tokai University School of Medicine, Shimokasuya 143, Isehara-shi, Kanagawa, 259-1143, Japan
| | - Masamichi Yagi
- Platform Technical Engineer Division, HPC and AI Business Department, System Platform Solution Unit, Fujitsu Limited, World Trade Center Building, 4-1, Hamamatsucho 2-chome, Minato-ku, Tokyo, 105-6125, Japan
| | - Atsuya Takeda
- Radiation Oncology Center, Ofuna Chuo Hospital, Kamakura-shi, Kanagawa, 247-0056, Japan
| | - Takeshi Akiba
- Department of Radiation Oncology, Tokai University School of Medicine, Shimokasuya 143, Isehara-shi, Kanagawa, 259-1143, Japan
| | - Eride Mutu
- Department of Radiation Oncology, Tokai University School of Medicine, Shimokasuya 143, Isehara-shi, Kanagawa, 259-1143, Japan
| | - Naoyuki Shigematsu
- Department of Radiology, Keio University School of Medicine, Shinanomachi 35, Shinjuku-ku, Tokyo, 160-8582, Japan
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Choi MS, Choi BS, Chung SY, Kim N, Chun J, Kim YB, Chang JS, Kim JS. Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer. Radiother Oncol 2020; 153:139-145. [PMID: 32991916 DOI: 10.1016/j.radonc.2020.09.045] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 09/22/2020] [Accepted: 09/23/2020] [Indexed: 12/21/2022]
Abstract
Manual segmentation is the gold standard method for radiation therapy planning; however, it is time-consuming and prone to inter- and intra-observer variation, giving rise to interests in auto-segmentation methods. We evaluated the feasibility of deep learning-based auto-segmentation (DLBAS) in comparison to commercially available atlas-based segmentation solutions (ABAS) for breast cancer radiation therapy. This study used contrast-enhanced planning computed tomography scans from 62 patients with breast cancer who underwent breast-conservation surgery. Contours of target volumes (CTVs), organs, and heart substructures were generated using two commercial ABAS solutions and DLBAS using fully convolutional DenseNet. The accuracy of the segmentation was assessed using 14 test patients using the Dice Similarity Coefficient and Hausdorff Distance referencing the expert contours. A sensitivity analysis was performed using non-contrast planning CT from 14 additional patients. Compared to ABAS, the proposed DLBAS model yielded more consistent results and the highest average Dice Similarity Coefficient values and lowest Hausdorff Distances, especially for CTVs and the substructures of the heart. ABAS showed limited performance in soft-tissue-based regions, such as the esophagus, cardiac arteries, and smaller CTVs. The results of sensitivity analysis between contrast and non-contrast CT test sets showed little difference in the performance of DLBAS and conversely, a large discrepancy for ABAS. The proposed DLBAS algorithm was more consistent and robust in its performance than ABAS across the majority of structures when examining both CTVs and normal organs. DLBAS has great potential to aid a key process in the radiation therapy workflow, helping optimise and reduce the clinical workload.
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Affiliation(s)
- Min Seo Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Byeong Su Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Seung Yeun Chung
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, South Korea
| | - Nalee Kim
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan School of Medicine, Seoul, South Korea
| | - Jaehee Chun
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Yong Bae Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Jee Suk Chang
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea.
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea.
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10
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Haq R, Hotca A, Apte A, Rimner A, Deasy JO, Thor M. Cardio-pulmonary substructure segmentation of radiotherapy computed tomography images using convolutional neural networks for clinical outcomes analysis. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2020; 14:61-66. [PMID: 33458316 PMCID: PMC7807536 DOI: 10.1016/j.phro.2020.05.009] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 05/21/2020] [Accepted: 05/29/2020] [Indexed: 02/06/2023]
Abstract
Background and purpose Radiation dose to the cardio-pulmonary system is critical for radiotherapy-induced mortality in non-small cell lung cancer. Our goal was to automatically segment substructures of the cardio-pulmonary system for use in outcomes analyses for thoracic cancers. We built and validated a multi-label Deep Learning Segmentation (DLS) model for accurate auto-segmentation of twelve cardio-pulmonary substructures. Materials and methods The DLS model utilized a convolutional neural network for segmenting substructures from 217 thoracic radiotherapy Computed Tomography (CT) scans. The model was built in the presence of variable image characteristics such as the absence/presence of contrast. We quantitatively evaluated the final model against expert contours for a hold-out dataset of 24 CT scans using Dice Similarity Coefficient (DSC), 95th Percentile of Hausdorff Distance and Dose-volume Histograms (DVH). DLS contours of an additional 25 scans were qualitatively evaluated by a radiation oncologist to determine their clinical acceptability. Results The DLS model reduced segmentation time per patient from about one hour to 10 s. Quantitatively, the highest accuracy was observed for the Heart (median DSC = (0.96 (0.95–0.97)). The median DSC for the remaining structures was between 0.81 and 0.93. No statistically significant difference was found between DVH metrics of the auto-generated and manual contours (p-value ⩾ 0.69). The expert judged that, on average, 85% of contours were qualitatively equivalent to state-of-the-art manual contouring. Conclusion The cardio-pulmonary DLS model performed well both quantitatively and qualitatively for all structures. This model has been incorporated into an open-source tool for the community to use for treatment planning and clinical outcomes analysis.
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Affiliation(s)
- Rabia Haq
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York NY 10017, USA
| | - Alexandra Hotca
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York NY 10017, USA
| | - Aditya Apte
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York NY 10017, USA
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York NY 10017, USA
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York NY 10017, USA
| | - Maria Thor
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York NY 10017, USA
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11
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Tran CT, Halicek M, Dormer JD, Tandon A, Hussain T, Fei B. Fully automated segmentation of the right ventricle in patients with repaired Tetralogy of Fallot using U-Net. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2020; 11317. [PMID: 32476706 DOI: 10.1117/12.2549052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Cardiac magnetic resonance (CMR) imaging is considered the standard imaging modality for volumetric analysis of the right ventricle (RV), an especially important practice in the evaluation of heart structure and function in patients with repaired Tetralogy of Fallot (rTOF). In clinical practice, however, this requires time-consuming manual delineation of the RV endocardium in multiple 2-dimensional (2D) slices at multiple phases of the cardiac cycle. In this work, we employed a U-Net based 2D convolutional neural network (CNN) classifier in the fully automatic segmentation of the RV blood pool. Our dataset was comprised of 5,729 short-axis cine CMR slices taken from 100 individuals with rTOF. Training of our CNN model was performed on images from 50 individuals while validation was performed on images from 10 individuals. Segmentation results were evaluated by Dice similarity coefficient (DSC) and Hausdorff distance (HD). Use of the CNN model on our testing group of 40 individuals yielded a median DSC of 90% and a median 95th percentile HD of 5.1 mm, demonstrating good performance in these metrics when compared to literature results. Our preliminary results suggest that our deep learning-based method can be effective in automating RV segmentation.
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Affiliation(s)
- Christopher T Tran
- University of Texas at Dallas, Department of Bioengineering, Richardson, TX, USA
| | - Martin Halicek
- University of Texas at Dallas, Department of Bioengineering, Richardson, TX, USA.,Georgia Inst. of Tech. and Emory Univ., Dept. of Biomedical Engineering, Atlanta, GA
| | - James D Dormer
- Georgia Inst. of Tech. and Emory Univ., Dept. of Biomedical Engineering, Atlanta, GA
| | - Animesh Tandon
- Dept. of Radiology, Univ. of Texas Southwestern Medical Center, Dallas, TX.,Department of Pediatrics, Univ. of Texas Southwestern Medical Center, Dallas, TX
| | - Tarique Hussain
- Dept. of Radiology, Univ. of Texas Southwestern Medical Center, Dallas, TX.,Department of Pediatrics, Univ. of Texas Southwestern Medical Center, Dallas, TX
| | - Baowei Fei
- University of Texas at Dallas, Department of Bioengineering, Richardson, TX, USA.,Advanced Imaging Research Center, Univ. of Texas Southwestern Medical Center, Dallas, TX.,Dept. of Radiology, Univ. of Texas Southwestern Medical Center, Dallas, TX
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Chen C, Qin C, Qiu H, Tarroni G, Duan J, Bai W, Rueckert D. Deep Learning for Cardiac Image Segmentation: A Review. Front Cardiovasc Med 2020; 7:25. [PMID: 32195270 PMCID: PMC7066212 DOI: 10.3389/fcvm.2020.00025] [Citation(s) in RCA: 288] [Impact Index Per Article: 72.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 02/17/2020] [Indexed: 12/15/2022] Open
Abstract
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound and major anatomical structures of interest (ventricles, atria, and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research.
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Affiliation(s)
- Chen Chen
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Chen Qin
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Huaqi Qiu
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Giacomo Tarroni
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
- CitAI Research Centre, Department of Computer Science, City University of London, London, United Kingdom
| | - Jinming Duan
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Wenjia Bai
- Data Science Institute, Imperial College London, London, United Kingdom
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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Benjamins JW, Hendriks T, Knuuti J, Juarez-Orozco LE, van der Harst P. A primer in artificial intelligence in cardiovascular medicine. Neth Heart J 2019; 27:392-402. [PMID: 31111458 PMCID: PMC6712147 DOI: 10.1007/s12471-019-1286-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Driven by recent developments in computational power, algorithms and web-based storage resources, machine learning (ML)-based artificial intelligence (AI) has quickly gained ground as the solution for many technological and societal challenges. AI education has become very popular and is oversubscribed at Dutch universities. Major investments were made in 2018 to develop and build the first AI-driven hospitals to improve patient care and reduce healthcare costs. AI has the potential to greatly enhance traditional statistical analyses in many domains and has been demonstrated to allow the discovery of 'hidden' information in highly complex datasets. As such, AI can also be of significant value in the diagnosis and treatment of cardiovascular disease, and the first applications of AI in the cardiovascular field are promising. However, many professionals in the cardiovascular field involved in patient care, education or science are unaware of the basics behind AI and the existing and expected applications in their field. In this review, we aim to introduce the broad cardiovascular community to the basics of modern ML-based AI and explain several of the commonly used algorithms. We also summarise their initial and future applications relevant to the cardiovascular field.
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Affiliation(s)
- J W Benjamins
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands
| | - T Hendriks
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands
| | - J Knuuti
- Turku PET Center, Turku University Hospital and University of Turku, Turku, Finland
| | - L E Juarez-Orozco
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands
- Turku PET Center, Turku University Hospital and University of Turku, Turku, Finland
| | - P van der Harst
- University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands.
- Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, The Netherlands.
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands.
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Haq R, Hotca A, Apte A, Rimner A, Deasy JO, Thor M. Cardio-Pulmonary Substructure Segmentation of CT Images Using Convolutional Neural Networks. ARTIFICIAL INTELLIGENCE IN RADIATION THERAPY 2019. [DOI: 10.1007/978-3-030-32486-5_20] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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