mlsanityData to 14 Oct 2026

CA-Diff: Collaborative Anatomy Diffusion for Brain Tissue Segmentation

arXiv 2506.22882 ↗First posted 28 Jun 2025eess.IV cs.CV cs.LGcited by 0

On the map: Diffusion models → Medical imaging and segmentation

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.

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.

Builds on

15 of 26 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025Conditional diffusion model with spatial attention and latent embedding for medical image segmentation10 Feb
  2. 2023Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation18 Mar
  3. 2021Label-Efficient Semantic Segmentation with Diffusion Models6 Dec

Other references · 12

  1. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  2. 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 586
  3. 2019ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networkscited by 575
  4. 2021Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysiscited by 422
  5. 2019Dynamic Convolution: Attention over Convolution Kernelscited by 356
  6. 2023One Transformer Fits All Distributions in Multi-Modal Diffusion at Scalecited by 187
  7. 2022Transformer Tracking with Cyclic Shifting Window Attentioncited by 75
  8. 2023Compact Transformer Tracker with Correlative Masked Modelingcited by 50
  9. 2023DiffusionTrack: Diffusion Model For Multi-Object Trackingcited by 40
  10. 2023DiffBFR: Bootstrapping Diffusion Model Towards Blind Face Restorationcited by 17
  11. 2024Video Anomaly Detection with Motion and Appearance Guided Patch Diffusion Modelcited by 14
  12. 2020ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentationcited by 10

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  2. 2025Medical Semantic Segmentation with Diffusion Pretrain
  3. 2025Guiding Registration with Emergent Similarity from Pre-Trained Diffusion Models
  4. 2024Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset
  5. 2023Ambiguous Medical Image Segmentation using Diffusion Models
  6. 2024Label-efficient multi-organ segmentation with a diffusion model
  7. 2024Label-Efficient 3D Brain Segmentation via Complementary 2D Diffusion Models with Orthogonal Views
  8. 2025MedCondDiff: Lightweight, Robust, Semantically Guided Diffusion for Medical Image Segmentation