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

Area of Science:

• Medical Image Analysis
• Artificial Intelligence in Healthcare
• Computational Anatomy

Background:

• Pancreas segmentation is crucial for medical image analysis but is challenging due to class imbalance, background noise, and non-rigid anatomical features.
• Existing methods often struggle to accurately delineate the pancreas, impacting downstream diagnostic and therapeutic applications.

Purpose of the Study:

• To develop an automated and accurate pancreas segmentation framework addressing key challenges in medical imaging.
• To leverage deep learning, specifically a deep Q network (DQN) and deformable U-Net, for improved pancreas localization and segmentation.

Main Methods:

• A deep Q network (DQN) was employed to learn a context-adaptive policy for precise pancreas localization via bounding box generation.
• A deformable U-Net architecture was utilized, incorporating geometrically deformable filters to capture intricate and geometry-aware features of the pancreas.
• The framework integrates contextual information and extracts anisotropic features for enhanced segmentation accuracy.

Main Results:

• The proposed DQN-driven deformable U-Net framework demonstrated effective pancreas segmentation on the NIH dataset.
• The approach successfully addressed challenges of class imbalance and complex anatomical variations.
• Quantitative and qualitative improvements in segmentation accuracy were observed compared to baseline methods.

Conclusions:

• The integrated DQN and deformable U-Net approach provides a robust solution for accurate pancreas segmentation in medical images.
• This framework offers a promising tool for enhancing medical image analysis and supporting clinical decision-making.
• Future work could explore the application of this method to other challenging segmentation tasks in medical imaging.