Enhancing Medical Image Segmentation via Heat Conduction Equation

TL;DR

This paper introduces a hybrid model combining state-space modules and Heat Conduction Operators to improve global context modeling in medical image segmentation, achieving state-of-the-art results on Abdomen CT data.

Contribution

The novel integration of Heat Conduction Equation with state-space modules in a hybrid architecture enhances semantic abstraction and long-range reasoning in medical segmentation models.

Findings

Achieved highest DSC of 0.8719 on Abdomen CT dataset.

Demonstrated scalable global diffusion improves segmentation accuracy.

Blended heat-based diffusion with state-space dynamics effectively models long-range dependencies.

Abstract

Medical image segmentation models struggle to achieve efficient global context modeling and long-range dependency reasoning under practical computational budgets. In this work, we propose a hybrid architecture utilizing U-Mamba with Heat Conduction Equation, which combines state-space modules for efficient long-range reasoning with Heat Conduction Operators (HCOs) in the bottleneck layers, simulating frequency-domain thermal diffusion for enhanced semantic abstraction. Experimental results show that our model attains the highest DSC (0.8719) on the Abdomen CT dataset. It suggests that blending state-space dynamics with heat-based global diffusion offers a scalable solution for medical segmentation tasks.