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arXiv · 1904.09120

Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net

Yunze Man·Yangsibo Huang·Junyi Feng·Xi Li·Fei Wu
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Abstract

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. Experiments on NIH dataset validate the effectiveness of the proposed framework in pancreas segmentation.

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Yunze Man, Yangsibo Huang, Junyi Feng, Xi Li, Fei Wu. 2019-04-19. Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net. https://doi.org/10.1109/tmi.2019.2911588

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