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

## TL;DR

This paper presents a novel approach combining Deep Q Networks and deformable U-Net to improve pancreas segmentation in medical images by addressing class imbalance and geometric variability.

## Contribution

It introduces a DQN-driven localization policy and deformable U-Net for geometry-aware feature extraction, enhancing segmentation accuracy.

## Key findings

- Effective pancreas localization with DQN policy
- Improved segmentation accuracy on NIH dataset
- Robustness to geometric variations

## 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.