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Yu Z, Li X, Li J, Chen W, Tang Z, Geng D. HSA-net with a novel CAD pipeline boosts both clinical brain tumor MR image classification and segmentation. Comput Biol Med 2024; 170:108039. [PMID: 38308874 DOI: 10.1016/j.compbiomed.2024.108039] [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: 10/19/2023] [Revised: 01/07/2024] [Accepted: 01/26/2024] [Indexed: 02/05/2024]
Abstract
Brain tumors are among the most prevalent neoplasms in current medical studies. Accurately distinguishing and classifying brain tumor types accurately is crucial for patient treatment and survival in clinical practice. However, existing computer-aided diagnostic pipelines are inadequate for practical medical use due to tumor complexity. In this study, we curated a multi-centre brain tumor dataset that includes various clinical brain tumor data types, including segmentation and classification annotations, surpassing previous efforts. To enhance brain tumor segmentation accuracy, we propose a new segmentation method: HSA-Net. This method utilizes the Shared Weight Dilated Convolution module (SWDC) and Hybrid Dense Dilated Convolution module (HDense) to capture multi-scale information while minimizing parameter count. The Effective Multi-Dimensional Attention (EMA) and Important Feature Attention (IFA) modules effectively aggregate task-related information. We introduce a novel clinical brain tumor computer-aided diagnosis pipeline (CAD) that combines HSA-Net with pipeline modification. This approach not only improves segmentation accuracy but also utilizes the segmentation mask as an additional channel feature to enhance brain tumor classification results. Our experimental evaluation of 3327 real clinical data demonstrates the effectiveness of the proposed method, achieving an average Dice coefficient of 86.85 % for segmentation and a classification accuracy of 95.35 %. We also validated the effectiveness of our proposed method using the publicly available BraTS dataset.
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Affiliation(s)
- Zekuan Yu
- Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China.
| | - Xiang Li
- Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China; School of Safety Science and Engineering, Anhui University of Science and Technology, Huainan, 232000, China
| | - Jiaxin Li
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Weiqiang Chen
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Zhiri Tang
- School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai, China
| | - Daoying Geng
- Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China; Huashan Hospital, Fudan University, Shanghai, 200040, China.
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Liu R, Gong G, Meng K, Du S, Yin Y. Hippocampal sparing in whole-brain radiotherapy for brain metastases: controversy, technology and the future. Front Oncol 2024; 14:1342669. [PMID: 38327749 PMCID: PMC10847568 DOI: 10.3389/fonc.2024.1342669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 01/15/2024] [Indexed: 02/09/2024] Open
Abstract
Whole-brain radiotherapy (WBRT) plays an irreplaceable role in the treatment of brain metastases (BMs), but cognitive decline after WBRT seriously affects patients' quality of life. The development of cognitive dysfunction is closely related to hippocampal injury, but standardized criteria for predicting hippocampal injury and dose limits for hippocampal protection have not yet been developed. This review systematically reviews the clinical efficacy of hippocampal avoidance - WBRT (HA-WBRT), the controversy over dose limits, common methods and characteristics of hippocampal imaging and segmentation, differences in hippocampal protection by common radiotherapy (RT) techniques, and the application of artificial intelligence (AI) and radiomic techniques for hippocampal protection. In the future, the application of new techniques and methods can improve the consistency of hippocampal dose limit determination and the prediction of the occurrence of cognitive dysfunction in WBRT patients, avoiding the occurrence of cognitive dysfunction in patients and thus benefiting more patients with BMs.
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Affiliation(s)
- Rui Liu
- Department of Graduate, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - GuanZhong Gong
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - KangNing Meng
- Department of Graduate, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - ShanShan Du
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yong Yin
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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Pospisil P, Hynkova L, Hnidakova L, Maistryszinova J, Slampa P, Kazda T. Unilateral hippocampal sparing during whole brain radiotherapy for multiple brain metastases: narrative and critical review. Front Oncol 2024; 14:1298605. [PMID: 38327742 PMCID: PMC10847587 DOI: 10.3389/fonc.2024.1298605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 01/08/2024] [Indexed: 02/09/2024] Open
Abstract
Background The landscape of brain metastases radiotherapy is evolving, with a shift away from whole-brain radiotherapy (WBRT) toward targeted stereotactic approaches aimed at preserving neurocognitive functions and maintaining overall quality of life. For patients with multiple metastases, especially in cases where targeted radiotherapy is no longer feasible due to widespread dissemination, the concept of hippocampal sparing radiotherapy (HA_WBRT) gains prominence. Methods In this narrative review we explore the role of the hippocampi in memory formation and the implications of their postradiotherapy lateral damage. We also consider the potential advantages of selectively sparing one hippocampus during whole-brain radiotherapy (WBRT). Additionally, by systematic evaluation of relevant papers published on PubMed database over last 20 years, we provide a comprehensive overview of the various changes that can occur in the left or right hippocampus as a consequence of radiotherapy. Results While it is important to note that various neurocognitive functions are interconnected throughout the brain, we can discern certain specialized roles of the hippocampi. The left hippocampus appears to play a predominant role in verbal memory, whereas the right hippocampus is associated more with visuospatial memory. Additionally, the anterior part of the hippocampus is more involved in episodic memory and emotional processing, while the posterior part is primarily responsible for spatial memory and pattern separation. Notably, a substantial body of evidence demonstrates a significant correlation between post-radiotherapy changes in the left hippocampus and subsequent cognitive decline in patients. Conclusion In the context of individualized palliative radiotherapy, sparing the unilateral (specifically, the left, which is dominant in most individuals) hippocampus could expand the repertoire of strategies available for adapted WBRT in cases involving multiple brain metastases where stereotactic radiotherapy is not a viable option. Prospective ongoing studies assessing various memory-sparing radiotherapy techniques will define new standard of radiotherapy care of patients with multiple brain metastases.
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Affiliation(s)
- Petr Pospisil
- Department of Radiation Oncology, Masaryk Memorial Cancer Institute, Brno, Czechia
- Department of Radiation Oncology, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Ludmila Hynkova
- Department of Radiation Oncology, Masaryk Memorial Cancer Institute, Brno, Czechia
- Department of Radiation Oncology, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Lucie Hnidakova
- Department of Radiation Oncology, Masaryk Memorial Cancer Institute, Brno, Czechia
- Department of Radiation Oncology, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Jana Maistryszinova
- Department of Radiation Oncology, Masaryk Memorial Cancer Institute, Brno, Czechia
- Department of Radiation Oncology, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Pavel Slampa
- Department of Radiation Oncology, Masaryk Memorial Cancer Institute, Brno, Czechia
- Department of Radiation Oncology, Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Tomas Kazda
- Department of Radiation Oncology, Masaryk Memorial Cancer Institute, Brno, Czechia
- Department of Radiation Oncology, Faculty of Medicine, Masaryk University, Brno, Czechia
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4
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Lei Y, Ding Y, Qiu RLJ, Wang T, Roper J, Fu Y, Shu HK, Mao H, Yang X. Hippocampus substructure segmentation using morphological vision transformer learning. Phys Med Biol 2023; 68:235013. [PMID: 37972414 PMCID: PMC10690959 DOI: 10.1088/1361-6560/ad0d45] [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: 06/12/2023] [Revised: 11/01/2023] [Accepted: 11/15/2023] [Indexed: 11/19/2023]
Abstract
The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniques prioritize hippocampal avoidance, which depends on an accurate segmentation of the small and complexly shaped hippocampus. To achieve accurate segmentation of the anterior and posterior regions of the hippocampus from T1 weighted (T1w) MR images, we developed a novel model, Hippo-Net, which uses a cascaded model strategy. The proposed model consists of two major parts: (1) a localization model is used to detect the volume-of-interest (VOI) of hippocampus. (2) An end-to-end morphological vision transformer network (Franchietal2020Pattern Recognit.102107246, Ranemetal2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW) pp 3710-3719) is used to perform substructures segmentation within the hippocampus VOI. The substructures include the anterior and posterior regions of the hippocampus, which are defined as the hippocampus proper and parts of the subiculum. The vision transformer incorporates the dominant features extracted from MR images, which are further improved by learning-based morphological operators. The integration of these morphological operators into the vision transformer increases the accuracy and ability to separate hippocampus structure into its two distinct substructures. A total of 260 T1w MRI datasets from medical segmentation decathlon dataset were used in this study. We conducted a five-fold cross-validation on the first 200 T1w MR images and then performed a hold-out test on the remaining 60 T1w MR images with the model trained on the first 200 images. In five-fold cross-validation, the Dice similarity coefficients were 0.900 ± 0.029 and 0.886 ± 0.031 for the hippocampus proper and parts of the subiculum, respectively. The mean surface distances (MSDs) were 0.426 ± 0.115 mm and 0.401 ± 0.100 mm for the hippocampus proper and parts of the subiculum, respectively. The proposed method showed great promise in automatically delineating hippocampus substructures on T1w MR images. It may facilitate the current clinical workflow and reduce the physicians' effort.
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Affiliation(s)
- Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Yifu Ding
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Richard L J Qiu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Tonghe Wang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States of America
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Yabo Fu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States of America
| | - Hui-Kuo Shu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
| | - Hui Mao
- Department of Radiology and Imaging Sciences and Winship Cancer Institute, Atlanta, GA 30308, United States of America
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America
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Li JN, Zhang SW, Qiang YR, Zhou QY. A novel cross-layer dual encoding-shared decoding network framework with spatial self-attention mechanism for hippocampus segmentation. Comput Biol Med 2023; 167:107584. [PMID: 37883852 DOI: 10.1016/j.compbiomed.2023.107584] [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: 04/10/2023] [Revised: 09/21/2023] [Accepted: 10/15/2023] [Indexed: 10/28/2023]
Abstract
Accurate segmentation of the hippocampus from the brain magnetic resonance images (MRIs) is a crucial task in the neuroimaging research, since its structural integrity is strongly related to several neurodegenerative disorders, such as Alzheimer's disease (AD). Automatic segmentation of the hippocampus structures is challenging due to the small volume, complex shape, low contrast and discontinuous boundaries of hippocampus. Although some methods have been developed for the hippocampus segmentation, most of them paid too much attention to the hippocampus shape and volume instead of considering the spatial information. Additionally, the extracted features are independent of each other, ignoring the correlation between the global and local information. In view of this, here we proposed a novel cross-layer dual Encoding-Shared Decoding network framework with Spatial self-Attention mechanism (called ESDSA) for hippocampus segmentation in human brains. Considering that the hippocampus is a relatively small part in MRI, we introduced the spatial self-attention mechanism in ESDSA to capture the spatial information of hippocampus for improving the segmentation accuracy. We also designed a cross-layer dual encoding-shared decoding network to effectively extract the global information of MRIs and the spatial information of hippocampus. The spatial features of hippocampus and the features extracted from the MRIs were combined to realize the hippocampus segmentation. Results on the baseline T1-weighted structural MRI data show that the performance of our ESDSA is superior to other state-of-the-art methods, and the dice similarity coefficient of ESDSA achieves 89.37%. In addition, the dice similarity coefficient of the Spatial Self-Attention mechanism (SSA) strategy and the dual Encoding-Shared Decoding (ESD) strategy is 9.47%, 5.35% higher than that of the baseline U-net, respectively, indicating that the strategies of SSA and ESD can effectively enhance the segmentation accuracy of human brain hippocampus.
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Affiliation(s)
- Jia-Ni Li
- MOE Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
| | - Shao-Wu Zhang
- MOE Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
| | - Yan-Rui Qiang
- MOE Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
| | - Qin-Yi Zhou
- MOE Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
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Turcas A, Leucuta D, Balan C, Clementel E, Gheara C, Kacso A, Kelly SM, Tanasa D, Cernea D, Achimas-Cadariu P. Deep-learning magnetic resonance imaging-based automatic segmentation for organs-at-risk in the brain: Accuracy and impact on dose distribution. Phys Imaging Radiat Oncol 2023; 27:100454. [PMID: 37333894 PMCID: PMC10276287 DOI: 10.1016/j.phro.2023.100454] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/27/2023] [Accepted: 05/31/2023] [Indexed: 06/20/2023] Open
Abstract
Background and purpose Normal tissue sparing in radiotherapy relies on proper delineation. While manual contouring is time consuming and subject to inter-observer variability, auto-contouring could optimize workflows and harmonize practice. We assessed the accuracy of a commercial, deep-learning, MRI-based tool for brain organs-at-risk delineation. Materials and methods Thirty adult brain tumor patients were retrospectively manually recontoured. Two additional structure sets were obtained: AI (artificial intelligence) and AIedit (manually corrected auto-contours). For 15 selected cases, identical plans were optimized for each structure set. We used Dice Similarity Coefficient (DSC) and mean surface-distance (MSD) for geometric comparison and gamma analysis and dose-volume-histogram comparison for dose metrics evaluation. Wilcoxon signed-ranks test was used for paired data, Spearman coefficient(ρ) for correlations and Bland-Altman plots to assess level of agreement. Results Auto-contouring was significantly faster than manual (1.1/20 min, p < 0.01). Median DSC and MSD were 0.7/0.9 mm for AI and 0.8/0.5 mm for AIedit. DSC was significantly correlated with structure size (ρ = 0.76, p < 0.01), with higher DSC for large structures. Median gamma pass rate was 74% (71-81%) for Plan_AI and 82% (75-86%) for Plan_AIedit, with no correlation with DSC or MSD. Differences between Dmean_AI and Dmean_Ref were ≤ 0.2 Gy (p < 0.05). The dose difference was moderately correlated with DSC. Bland Altman plot showed minimal discrepancy (0.1/0) between AI and reference Dmean/Dmax. Conclusions The AI-model showed good accuracy for large structures, but developments are required for smaller ones. Auto-segmentation was significantly faster, with minor differences in dose distribution caused by geometric variations.
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Affiliation(s)
- Andrada Turcas
- The European Organisation for Research and Treatment of Cancer (EORTC) Headquarters, RTQA, Brussels, Belgium
- SIOP Europe, The European Society for Paediatric Oncology (SIOPE), QUARTET Project, Brussels, Belgium
- University of Medicine and Pharmacy and Medicine “Iuliu Hatieganu”, Oncology Department, Cluj-Napoca, Romania
- Oncology Institute “Prof. Dr. Ion Chiricuta”, Radiotherapy Department, Cluj-Napoca, Romania
| | - Daniel Leucuta
- University of Medicine and Pharmacy “Iuliu Hatieganu”, Department of Medical Informatics and Biostatistics, Cluj-Napoca, Romania
| | - Cristina Balan
- Oncology Institute “Prof. Dr. Ion Chiricuta”, Radiotherapy Department, Cluj-Napoca, Romania
- “Babes-Bolyai” University, Faculty of Physics, Cluj-Napoca, Romania
| | - Enrico Clementel
- The European Organisation for Research and Treatment of Cancer (EORTC) Headquarters, RTQA, Brussels, Belgium
| | - Cristina Gheara
- Oncology Institute “Prof. Dr. Ion Chiricuta”, Radiotherapy Department, Cluj-Napoca, Romania
- “Babes-Bolyai” University, Faculty of Physics, Cluj-Napoca, Romania
| | - Alex Kacso
- University of Medicine and Pharmacy and Medicine “Iuliu Hatieganu”, Oncology Department, Cluj-Napoca, Romania
- Oncology Institute “Prof. Dr. Ion Chiricuta”, Radiotherapy Department, Cluj-Napoca, Romania
| | - Sarah M. Kelly
- The European Organisation for Research and Treatment of Cancer (EORTC) Headquarters, RTQA, Brussels, Belgium
- SIOP Europe, The European Society for Paediatric Oncology (SIOPE), QUARTET Project, Brussels, Belgium
| | - Delia Tanasa
- Oncology Institute “Prof. Dr. Ion Chiricuta”, Radiotherapy Department, Cluj-Napoca, Romania
| | - Dana Cernea
- Oncology Institute “Prof. Dr. Ion Chiricuta”, Radiotherapy Department, Cluj-Napoca, Romania
| | - Patriciu Achimas-Cadariu
- University of Medicine and Pharmacy and Medicine “Iuliu Hatieganu”, Oncology Department, Cluj-Napoca, Romania
- Oncology Institute “Prof. Dr. Ion Chiricuta”, Surgery Department, Cluj-Napoca, Romania
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Lin CY, Chou LS, Wu YH, Kuo JS, Mehta MP, Shiau AC, Liang JA, Hsu SM, Wang TH. Developing an AI-assisted planning pipeline for hippocampal avoidance whole brain radiotherapy. Radiother Oncol 2023; 181:109528. [PMID: 36773828 DOI: 10.1016/j.radonc.2023.109528] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 01/04/2023] [Accepted: 02/03/2023] [Indexed: 02/12/2023]
Abstract
BACKGROUND AND PURPOSE Hippocampal avoidance whole brain radiotherapy (HA-WBRT) is effective for controlling disease and preserving neuro-cognitive function for brain metastases. However, contouring and planning of HA-WBRT is complex and time-consuming. We designed and evaluated a pipeline using deep learning tools for a fully automated treatment planning workflow to generate HA-WBRT radiotherapy plans. MATERIALS AND METHODS We retrospectively collected 50 adult patients who received HA-WBRT. Using RTOG- 0933 clinical trial protocol guidelines, all organs-at-risk (OARs) and the clinical target volume (CTV) were contoured by experienced radiation oncologists. A deep-learning segmentation model was designed and trained. Next, we developed a volumetric-modulated arc therapy (VMAT) auto-planning algorithm for 30 Gy in 10 fractions. Automated segmentations were evaluated using the Dice similarity coefficient (DSC) and 95th-percentile Hausdorff distance (95 % HD). Auto-plans were evaluated by the percentage of PTV volume that receives 30 Gy (V30Gy), conformity index (CI), and homogeneity index (HI) of planning target volume (PTV) and the minimum dose (D100%) and maximum dose (Dmax) for the hippocampus, Dmax for the lens, eyes, optic nerve, brain stem, and chiasm. RESULTS We developed a deep-learning segmentation model and an auto-planning script. For the 10 cases in the independent test set, the overall average DSC and 95 % HD of contours were greater than 0.8 and less than 7 mm, respectively. All auto-plans met the RTOG- 0933 criteria. The HA-WBRT plan automatically created time was about 10 min. CONCLUSIONS An artificial intelligence (AI)-assisted pipeline using deep learning tools can rapidly and accurately generate clinically acceptable HA-WBRT plans with minimal manual intervention and increase efficiency of this treatment for brain metastases.
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Affiliation(s)
- Chih-Yuan Lin
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan; Medical Physics and Radiation Measurements Laboratory, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Lin-Shan Chou
- Division of Radiation Oncology, Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yuan-Hung Wu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan; Medical Physics and Radiation Measurements Laboratory, National Yang Ming Chiao Tung University, Taipei, Taiwan; Division of Radiation Oncology, Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - John S Kuo
- Neuroscience and Brain Disease Center, China Medical University, Taichung, Taiwan; Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan; Department of Neurosurgery, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - Minesh P Mehta
- Department of Radiation Oncology, Miami Cancer Institute, Baptist Health South Florida, Miami, Florida, USA; Florida International University, Miami, Florida, USA
| | - An-Cheng Shiau
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan; Department of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan
| | - Ji-An Liang
- Department of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan; Department of Medicine, China Medical University, Taichung, Taiwan
| | - Shih-Ming Hsu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan; Medical Physics and Radiation Measurements Laboratory, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Ti-Hao Wang
- Department of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan.
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Mackay K, Bernstein D, Glocker B, Kamnitsas K, Taylor A. A Review of the Metrics Used to Assess Auto-Contouring Systems in Radiotherapy. Clin Oncol (R Coll Radiol) 2023; 35:354-369. [PMID: 36803407 DOI: 10.1016/j.clon.2023.01.016] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 12/05/2022] [Accepted: 01/23/2023] [Indexed: 02/01/2023]
Abstract
Auto-contouring could revolutionise future planning of radiotherapy treatment. The lack of consensus on how to assess and validate auto-contouring systems currently limits clinical use. This review formally quantifies the assessment metrics used in studies published during one calendar year and assesses the need for standardised practice. A PubMed literature search was undertaken for papers evaluating radiotherapy auto-contouring published during 2021. Papers were assessed for types of metric and the methodology used to generate ground-truth comparators. Our PubMed search identified 212 studies, of which 117 met the criteria for clinical review. Geometric assessment metrics were used in 116 of 117 studies (99.1%). This includes the Dice Similarity Coefficient used in 113 (96.6%) studies. Clinically relevant metrics, such as qualitative, dosimetric and time-saving metrics, were less frequently used in 22 (18.8%), 27 (23.1%) and 18 (15.4%) of 117 studies, respectively. There was heterogeneity within each category of metric. Over 90 different names for geometric measures were used. Methods for qualitative assessment were different in all but two papers. Variation existed in the methods used to generate radiotherapy plans for dosimetric assessment. Consideration of editing time was only given in 11 (9.4%) papers. A single manual contour as a ground-truth comparator was used in 65 (55.6%) studies. Only 31 (26.5%) studies compared auto-contours to usual inter- and/or intra-observer variation. In conclusion, significant variation exists in how research papers currently assess the accuracy of automatically generated contours. Geometric measures are the most popular, however their clinical utility is unknown. There is heterogeneity in the methods used to perform clinical assessment. Considering the different stages of system implementation may provide a framework to decide the most appropriate metrics. This analysis supports the need for a consensus on the clinical implementation of auto-contouring.
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Affiliation(s)
- K Mackay
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK.
| | - D Bernstein
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK
| | - B Glocker
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - K Kamnitsas
- Department of Computing, Imperial College London, South Kensington Campus, London, UK; Department of Engineering Science, University of Oxford, Oxford, UK
| | - A Taylor
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK
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9
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Huang X, Chen J, Chen M, Chen L, Wan Y. TDD-UNet:Transformer with double decoder UNet for COVID-19 lesions segmentation. Comput Biol Med 2022; 151:106306. [PMID: 36403357 PMCID: PMC9664702 DOI: 10.1016/j.compbiomed.2022.106306] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 10/22/2022] [Accepted: 11/06/2022] [Indexed: 11/09/2022]
Abstract
The outbreak of new coronary pneumonia has brought severe health risks to the world. Detection of COVID-19 based on the UNet network has attracted widespread attention in medical image segmentation. However, the traditional UNet model is challenging to capture the long-range dependence of the image due to the limitations of the convolution kernel with a fixed receptive field. The Transformer Encoder overcomes the long-range dependence problem. However, the Transformer-based segmentation approach cannot effectively capture the fine-grained details. We propose a transformer with a double decoder UNet for COVID-19 lesions segmentation to address this challenge, TDD-UNet. We introduce the multi-head self-attention of the Transformer to the UNet encoding layer to extract global context information. The dual decoder structure is used to improve the result of foreground segmentation by predicting the background and applying deep supervision. We performed quantitative analysis and comparison for our proposed method on four public datasets with different modalities, including CT and CXR, to demonstrate its effectiveness and generality in segmenting COVID-19 lesions. We also performed ablation studies on the COVID-19-CT-505 dataset to verify the effectiveness of the key components of our proposed model. The proposed TDD-UNet also achieves higher Dice and Jaccard mean scores and the lowest standard deviation compared to competitors. Our proposed method achieves better segmentation results than other state-of-the-art methods.
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Affiliation(s)
- Xuping Huang
- Computer School, University of South China, Hengyang 421001, China
| | - Junxi Chen
- Affiliated Nanhua Hospital, University of South China, Hengyang 421001, China
| | - Mingzhi Chen
- College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
| | - Lingna Chen
- Computer School, University of South China, Hengyang 421001, China.
| | - Yaping Wan
- Computer School, University of South China, Hengyang 421001, China.
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Xu X, Wen Y, Zhao L, Zhang Y, Zhao Y, Tang Z, Yang Z, Chen CY. CARes-UNet: Content-aware residual UNet for lesion segmentation of COVID-19 from chest CT images. Med Phys 2021; 48:7127-7140. [PMID: 34528263 PMCID: PMC8646636 DOI: 10.1002/mp.15231] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 08/23/2021] [Accepted: 09/11/2021] [Indexed: 12/28/2022] Open
Abstract
PURPOSE Coronavirus disease 2019 (COVID-19) has caused a serious global health crisis. It has been proven that the deep learning method has great potential to assist doctors in diagnosing COVID-19 by automatically segmenting the lesions in computed tomography (CT) slices. However, there are still several challenges restricting the application of these methods, including high variation in lesion characteristics and low contrast between lesion areas and healthy tissues. Moreover, the lack of high-quality labeled samples and large number of patients lead to the urgency to develop a high accuracy model, which performs well not only under supervision but also with semi-supervised methods. METHODS We propose a content-aware lung infection segmentation deep residual network (content-aware residual UNet (CARes-UNet)) to segment the lesion areas of COVID-19 from the chest CT slices. In our CARes-UNet, the residual connection was used in the convolutional block, which alleviated the degradation problem during the training. Then, the content-aware upsampling modules were introduced to improve the performance of the model while reducing the computation cost. Moreover, to achieve faster convergence, an advanced optimizer named Ranger was utilized to update the model's parameters during training. Finally, we employed a semi-supervised segmentation framework to deal with the problem of lacking pixel-level labeled data. RESULTS We evaluated our approach using three public datasets with multiple metrics and compared its performance to several models. Our method outperforms other models in multiple indicators, for instance in terms of Dice coefficient on COVID-SemiSeg Dataset, CARes-UNet got the score 0.731, and semi-CARes-UNet further boosted it to 0.776. More ablation studies were done and validated the effectiveness of each key component of our proposed model. CONCLUSIONS Compared with the existing neural network methods applied to the COVID-19 lesion segmentation tasks, our CARes-UNet can gain more accurate segmentation results, and semi-CARes-UNet can further improve it using semi-supervised learning methods while presenting a possible way to solve the problem of lack of high-quality annotated samples. Our CARes-UNet and semi-CARes-UNet can be used in artificial intelligence-empowered computer-aided diagnosis system to improve diagnostic accuracy in this ongoing COVID-19 pandemic.
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Affiliation(s)
- Xinhua Xu
- Artificial Intelligence Medical CenterSchool of Intelligent Systems EngineeringSun Yat‐sen UniversityShenzhenChina
| | - Yuhang Wen
- Artificial Intelligence Medical CenterSchool of Intelligent Systems EngineeringSun Yat‐sen UniversityShenzhenChina
| | - Lu Zhao
- Department of Clinical LaboratoryThe Sixth Affiliated HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Yi Zhang
- Artificial Intelligence Medical CenterSchool of Intelligent Systems EngineeringSun Yat‐sen UniversityShenzhenChina
| | - Youjun Zhao
- Artificial Intelligence Medical CenterSchool of Intelligent Systems EngineeringSun Yat‐sen UniversityShenzhenChina
| | - Zixuan Tang
- Artificial Intelligence Medical CenterSchool of Intelligent Systems EngineeringSun Yat‐sen UniversityShenzhenChina
| | - Ziduo Yang
- Artificial Intelligence Medical CenterSchool of Intelligent Systems EngineeringSun Yat‐sen UniversityShenzhenChina
| | - Calvin Yu‐Chian Chen
- Artificial Intelligence Medical CenterSchool of Intelligent Systems EngineeringSun Yat‐sen UniversityShenzhenChina
- Department of Medical ResearchChina Medical University HospitalTaichungTaiwan
- Department of Bioinformatics and Medical EngineeringAsia UniversityTaichungTaiwan
- Guangdong Provincial Key Laboratory of Fire Science and TechnologyGuangzhouChina
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