Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net
This work addresses pancreas segmentation for medical image analysis, but it appears incremental as it combines existing techniques like DQN and U-Net with modifications.
The paper tackled pancreas segmentation in medical images by introducing a Deep Q Network (DQN) with a deformable U-Net to address class imbalance and non-rigid geometry, achieving validation on the NIH dataset.
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.