期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 38, 期 8, 页码 1971-1980出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2019.2911588
关键词
Computational and artificial intelligence; image processing; image segmentation; imaging tomography; computed tomography; pancreas segmentation; DQN; deformable U-net
类别
资金
- Zhejiang Provincial Natural Science Foundation of China [LR19F020004]
- National Basic Research Program of China [2015CB352302]
- National Natural Science Foundation of China [U1509206, 61751209]
- Zhejiang University K.P.Chao's High Technology Development Foundation
- Tencent AI Lab Rhino-Bird Joint Research Program [JR201806]
The segmentation of pancreas is important for medical image analysis, yet it faces great challenges of class imbalance, background distractions, and non-rigid geometrical features. To address these difficulties, we introduce a deep Q network (DQN) driven approach with deformable U-Net to accurately segment the pancreas by explicitly interacting with contextual information and extract anisotropic features from pancreas. The DQN-based model learns a context-adaptive localization policy to produce a visually tightened and precise localization bounding box of the pancreas. Furthermore, deformable U-Net captures geometry aware information of pancreas by learning geometrically deformable filters for feature extraction. The experiments on NIH dataset validate the effectiveness of the proposed framework in pancreas segmentation.
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