DiffAtlas: GenAI-fying Atlas Segmentation via Image-Mask Diffusion
Journal:
arXiv
Published Date:
Mar 9, 2025
Abstract
Accurate medical image segmentation is crucial for precise anatomical
delineation. Deep learning models like U-Net have shown great success but
depend heavily on large datasets and struggle with domain shifts, complex
structures, and limited training samples. Recent studies have explored
diffusion models for segmentation by iteratively refining masks. However, these
methods still retain the conventional image-to-mask mapping, making them highly
sensitive to input data, which hampers stability and generalization. In
contrast, we introduce DiffAtlas, a novel generative framework that models both
images and masks through diffusion during training, effectively ``GenAI-fying''
atlas-based segmentation. During testing, the model is guided to generate a
specific target image-mask pair, from which the corresponding mask is obtained.
DiffAtlas retains the robustness of the atlas paradigm while overcoming its
scalability and domain-specific limitations. Extensive experiments on CT and
MRI across same-domain, cross-modality, varying-domain, and different
data-scale settings using the MMWHS and TotalSegmentator datasets demonstrate
that our approach outperforms existing methods, particularly in limited-data
and zero-shot modality segmentation. Code is available at
https://github.com/M3DV/DiffAtlas.
