# CA-Diff: Collaborative Anatomy Diffusion for Brain Tissue Segmentation

arXiv: https://arxiv.org/abs/2506.22882 · HTML: https://mlsanity.com/paper/2506.22882/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 28 Jun 2025.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 15 of 15)

- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Label-Efficient Semantic Segmentation with Diffusion Models](https://mlsanity.com/paper/2112.03126/) (2021)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [Dynamic Convolution: Attention over Convolution Kernels](https://mlsanity.com/paper/1912.03458/) (2019)
- [One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale](https://mlsanity.com/paper/2303.06555/) (2023)
- [Transformer Tracking with Cyclic Shifting Window Attention](https://mlsanity.com/paper/2205.03806/) (2022)
- [Compact Transformer Tracker with Correlative Masked Modeling](https://mlsanity.com/paper/2301.10938/) (2023)
- [Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation](https://mlsanity.com/paper/2303.10326/) (2023)
- [DiffusionTrack: Diffusion Model For Multi-Object Tracking](https://mlsanity.com/paper/2308.09905/) (2023)
- [DiffBFR: Bootstrapping Diffusion Model Towards Blind Face Restoration](https://mlsanity.com/paper/2305.04517/) (2023)
- [Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Model](https://mlsanity.com/paper/2412.09026/) (2024)
- [ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation](https://mlsanity.com/paper/2002.05773/) (2020)
- [Conditional diffusion model with spatial attention and latent embedding for medical image segmentation](https://mlsanity.com/paper/2502.06997/) (2025)

## Abstract (arXiv)

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.