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Diff-UNet: A diffusion embedded network for robust 3D medical image segmentation.

Zhaohu Xing¹, Liang Wan², Huazhu Fu³

¹The Hong Kong University of Science and Technology (Guangzhou), PR China.

Medical Image Analysis

|July 2, 2025

Summary

This summary is machine-generated.

We introduce Diff-UNet, a novel diffusion-based model for 3D medical image segmentation. It effectively captures inter-slice relationships and improves segmentation accuracy, especially for complex structures.

Keywords:

3D medical image segmentation Boundary prediction Diffusion model Uncertainty estimation

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Diffusion models show promise for 2D medical image segmentation.
Direct extension to 3D segmentation is limited by ignored inter-slice data relationships and high computational costs.

Purpose of the Study:

To develop a diffusion-based model for effective 3D medical image segmentation.
To address limitations of existing 2D-based methods in 3D contexts.

Main Methods:

Introduced Diff-UNet, a two-branch diffusion model for 3D segmentation.
Incorporated a boundary-prediction branch and a Multi-granularity Boundary Aggregation (MBA) module.
Utilized Monte Carlo Diffusion (MC-Diff) for uncertainty mapping and an uncertainty-guided loss.
Implemented a Progressive Uncertainty-driven REfinement (PURE) strategy during inference.

Main Results:

Diff-UNet quantitatively and qualitatively outperforms state-of-the-art methods on BraTS2023, SegRap2023, and AIIB2023 datasets.
Demonstrated superior performance on segmenting small or complex anatomical structures.
Validated across diverse organs and imaging modalities.

Conclusions:

Diff-UNet offers a significant advancement in 3D medical image segmentation using diffusion models.
The proposed boundary-aware and uncertainty-guided approach enhances segmentation accuracy and robustness.
The model's effectiveness is confirmed on large-scale, diverse medical imaging datasets.