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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

FEU-Diff: A Diffusion Model With Fuzzy Evidence-Driven Dynamic Uncertainty Fusion for Medical Image Segmentation.

Sheng Geng, Shu Jiang, Tao Hou

IEEE Transactions on Neural Networks and Learning Systems

|September 16, 2025

Summary

This summary is machine-generated.

FEU-Diff enhances medical image segmentation using diffusion models by integrating fuzzy evidence and uncertainty fusion. This approach improves accuracy and detail preservation in segmentation results.

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Area of Science:

Artificial Intelligence
Medical Imaging
Computer Vision

Background:

Diffusion models are emerging generative frameworks for medical image segmentation.
Current methods struggle with adaptive fusion of conditional priors and denoised features.
Existing approaches lack explicit modeling of pixel-level uncertainty, leading to detail loss.

Purpose of the Study:

To introduce FEU-Diff, a novel diffusion-based segmentation framework.
To address limitations in adaptive fusion and uncertainty modeling in diffusion models.
To improve accuracy and structural detail preservation in medical image segmentation.

Main Methods:

FEU-Diff integrates fuzzy evidence modeling and uncertainty fusion (UF).
A fuzzy semantic enhancement (FSE) module models pixel-level uncertainty using Gaussian membership functions and fuzzy logic.
An evidence dynamic fusion (EDF) module uses Dirichlet distribution for adaptive feature fusion and UF quantifies prediction discrepancies.

Main Results:

FEU-Diff outperforms state-of-the-art methods on four public datasets.
Achieved average improvements: 1.42% Dice Similarity Coefficient (DSC), 1.47% Intersection over Union (IoU).
Reduced 95th percentile Hausdorff distance (HD95) by 2.26 mm and generated interpretable uncertainty maps.

Conclusions:

FEU-Diff effectively improves medical image segmentation accuracy and completeness.
The framework's fuzzy evidence and uncertainty fusion mechanisms enhance boundary delineation and detail preservation.
FEU-Diff offers enhanced clinical interpretability through generated uncertainty maps.