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

arXiv: https://arxiv.org/abs/1904.09120 · HTML: https://mlsanity.com/paper/1904.09120/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2019.

## Built on by (most cited first, 15 of 15)

- [Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2110.08762/) (2021)
- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [Multi-task Federated Learning for Heterogeneous Pancreas Segmentation](https://mlsanity.com/paper/2108.08537/) (2021)
- [Hierarchical 3D Feature Learning for Pancreas Segmentation](https://mlsanity.com/paper/2109.01667/) (2021)
- [No More Sliding Window: Efficient 3D Medical Image Segmentation with Differentiable Top-k Patch Sampling](https://mlsanity.com/paper/2501.10814/) (2025)
- [Semi-weakly Supervised Contrastive Representation Learning for Retinal Fundus Images](https://mlsanity.com/paper/2108.02122/) (2021)
- [A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation](https://mlsanity.com/paper/2309.03906/) (2023)
- [Deep Learning for Pancreas Segmentation: a Systematic Review](https://mlsanity.com/paper/2407.16313/) (2024)
- [Dynamic U-Net: Adaptively Calibrate Features for Abdominal Multi-organ Segmentation](https://mlsanity.com/paper/2403.07303/) (2024)
- [Medical Instrument Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning](https://mlsanity.com/paper/2107.14476/) (2021)
- [MiniGPT-Pancreas: Multimodal Large Language Model for Pancreas Cancer Classification and Detection](https://mlsanity.com/paper/2412.15925/) (2024)
- [Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions](https://mlsanity.com/paper/2107.11517/) (2021)
- [Detection and Segmentation of Pancreas using Morphological Snakes and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2302.06356/) (2023)
- [Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning](https://mlsanity.com/paper/2405.12367/) (2024)
- [Anatomically-aware conformal prediction for medical image segmentation with random walks](https://mlsanity.com/paper/2601.18997/) (2026)

## Abstract (arXiv)

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.