FocusNetv2: Imbalanced large and small organ segmentation with adversarial shape constraint for head and neck CT images |
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| Institution: | 1. Cancer Hospital of University of the Chinese Academy of Sciences (Zhejiang Cancer Hospital), China;2. Department of Computer Science, Rutgers University, Piscataway, NJ, USA;3. SenseTime Research, China;4. Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China;1. Medical School of Nanjing University, Nanjing, China;2. National Institute of Healthcare Data Science at Nanjing University, Nanjing, China;3. School of Mathematics and Statistics, Xi’an Jiaotong University, Shanxi, China;4. Department of Psychiatry and Behavioral Sciences and the Department of Computer Science, Stanford University, CA, USA;5. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;6. Department of Radiology, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, China;7. School of Biomedical Engineering, ShanghaiTech University, Shanghai, China;8. Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China;9. Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea;1. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, PR China;2. Department of Radiation Oncology, West China Hospital, Sichuan University, Chengdu 610041, PR China;1. Department of Radiology and Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;2. Department of Radiation Oncology, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;3. School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China;4. School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China;5. Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200030, China;6. Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea |
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| Abstract: | Radiotherapy is a treatment where radiation is used to eliminate cancer cells. The delineation of organs-at-risk (OARs) is a vital step in radiotherapy treatment planning to avoid damage to healthy organs. For nasopharyngeal cancer, more than 20 OARs are needed to be precisely segmented in advance. The challenge of this task lies in complex anatomical structure, low-contrast organ contours, and the extremely imbalanced size between large and small organs. Common segmentation methods that treat them equally would generally lead to inaccurate small-organ labeling. We propose a novel two-stage deep neural network, FocusNetv2, to solve this challenging problem by automatically locating, ROI-pooling, and segmenting small organs with specifically designed small-organ localization and segmentation sub-networks while maintaining the accuracy of large organ segmentation. In addition to our original FocusNet, we employ a novel adversarial shape constraint on small organs to ensure the consistency between estimated small-organ shapes and organ shape prior knowledge. Our proposed framework is extensively tested on both self-collected dataset of 1,164 CT scans and the MICCAI Head and Neck Auto Segmentation Challenge 2015 dataset, which shows superior performance compared with state-of-the-art head and neck OAR segmentation methods. |
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