CA-Diff: Collaborative Anatomy Diffusion for Brain Tissue Segmentation

2025/06/28 by Qilong Xing, Xing, Qilong, Zikai Song +13
Computer Science · Medicine · Neuroscience · #Advanced Neuroimaging Techniques and Applications #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Medical Image Segmentation Techniques #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2506.22882

openalex publication_date 2025/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Segmentation of brain structures from MRI is crucial for evaluating brain morphology, yet existing CNN and transformer-based methods struggle to delineate complex structures accurately. While current diffusion models have shown promise in image segmentation, they are inadequate when applied directly to brain MRI due to neglecting anatomical information. To address this, we propose Collaborative Anatomy Diffusion (CA-Diff), a framework integrating spatial anatomical features to enhance segmentation accuracy of the diffusion model. Specifically, we introduce distance field as an auxiliary anatomical condition to provide global spatial context, alongside a collaborative diffusion process to model its joint distribution with anatomical structures, enabling effective utilization of anatomical features for segmentation. Furthermore, we introduce a consistency loss to refine relationships between the distance field and anatomical structures and design a time adapted channel attention module to enhance the U-Net feature fusion procedure. Extensive experiments show that CA-Diff outperforms state-of-the-art (SOTA) methods.

Citations

• Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Model
• Conditional diffusion model with spatial attention and latent embedding for medical image segmentation
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• DiffBFR: Bootstrapping Diffusion Model Towards Blind Face Restoration
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• Automated Design of Deep Learning Methods for Biomedical Image Segmentation
• UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
• Dynamic Convolution: Attention over Convolution Kernels
• ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
• ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
• V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

Related

• Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation
• Learning Anatomical Segmentations for Tractography from Diffusion MRI
• VGDM: Vision-Guided Diffusion Model for Brain Tumor Detection and Segmentation
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI
• MedCondDiff: Lightweight, Robust, Semantically Guided Diffusion for Medical Image Segmentation