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ShanghaiTech University Knowledge Management System
Morphology-Guided Prostate MRI Segmentation with Multi-slice Association | |
2021-07-01 | |
会议录名称 | LECTURE NOTES IN COMPUTER SCIENCE (IF:0.402[JCR-2005],0.000[5-Year]) |
ISSN | 0302-9743 |
卷号 | 12966 LNCS |
页码 | 507-516 |
发表状态 | 已发表 |
DOI | 10.1007/978-3-030-87589-3_52 |
摘要 | Prostate segmentation from magnetic resonance (MR) images plays an important role in prostate cancer diagnosis and treatment. Previous works typically overlooked large variations of prostate shapes, especially on the boundary area. Furthermore, the small glandular areas at ending slices also make the task very challenging. To overcome these problems, this paper presents a two-stage framework that explicitly utilizes prostate morphological representations (e.g., point, boundary) to accurately localize the prostate region with a coarse volumetric segmentation. Based on the 3D coarse outputs of the first stage, a 2D segmentation network with multi-slice association is further introduced to produce more reliable and accurate segmentation, due to large slice thickness in prostate MR images. Besides, several novel loss functions are further designed to enhance the consistency of prostate boundaries. Extensive experiments on large prostate MRI dataset show superior performance of our proposed method compared to several state-of-the-art methods. © 2021, Springer Nature Switzerland AG. |
关键词 | Diagnosis Diseases Image segmentation Large dataset Magnetic resonance Magnetic resonance imaging Medical imaging Morphology 2D segmentation Cancer diagnosis Morphological representation MRI segmentation Multi slices Multi slice association Prostate cancers Prostate MRI segmentation Prostate segmentation Volumetric segmentations |
会议名称 | 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 |
会议地点 | Virtual, Online |
会议日期 | September 27, 2021 - September 27, 2021 |
收录类别 | EI |
语种 | 英语 |
出版者 | Springer Science and Business Media Deutschland GmbH |
EI入藏号 | 20214110990346 |
EI主题词 | Urology |
EISSN | 1611-3349 |
EI分类号 | 461.1 Biomedical Engineering ; 461.6 Medicine and Pharmacology ; 701.2 Magnetism: Basic Concepts and Phenomena ; 723.2 Data Processing and Image Processing ; 746 Imaging Techniques ; 931.2 Physical Properties of Gases, Liquids and Solids ; 951 Materials Science |
原始文献类型 | Conference article (CA) |
引用统计 | 正在获取...
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文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/133493 |
专题 | 信息科学与技术学院_本科生 生物医学工程学院_PI研究组_沈定刚组 |
通讯作者 | Feng, Jun |
作者单位 | 1.School of Information Science and Technology, Northwest University, Xi’an, China; 2.Department of Computer Science, The University of Hong Kong, Hong Kong; 3.School of Electrical and Information Engineering, Shandong University, Weihai, China; 4.School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China; 5.Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; 6.School of Biomedical Engineering, ShanghaiTech University, Shanghai, China |
推荐引用方式 GB/T 7714 | Li, Jianping,Cui, Zhiming,Wang, Shuai,et al. Morphology-Guided Prostate MRI Segmentation with Multi-slice Association[C]:Springer Science and Business Media Deutschland GmbH,2021:507-516. |
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