Unsupervised Domain Adaptation in Biomedical Images Segmentation With Guided Diffusion Generative Prior

Semantic segmentation has suffered for a while from a lack of datasets such as ImageNet for image classification. This issue was partially alleviated by the advent of the segment anything model (SAM), which provides a foundation model trained on the largest and most diverse segmentation dataset to date. However, the SAM often falls short in segmenting specific regions, mostly in regard to biomedical images; this is why unsupervised domain adaptation (UDA) remains the best option for addressing the challenge of generalization capabilities. Classical UDA methods might be ineffective in several biomedical segmentation cases because the gap between two datasets, named domain shift, is too high. To address this issue, we propose a strategy based on learning the source mask probability distribution with a segmentation diffusion model as a generative prior to propose accurate target segmentation at inference. This latter can be guided by supplementary inputs, which allows us to call for the rich information contained in SAM raw segmentation both to perform adaptation and to improve robustness. A study was conducted using a comprehensive collection of segmentation datasets: 3 domains for mitochondria, 2 for the endoplasmic reticulum, and 2 for brain tumors, allowing the creation of 10 adaptation scenarios and providing an extensive test basis. The results of the experiments reveal that our proposed method outperforms various state-of-the-art UDA methods. Furthermore, ablation studies highlight the significant role of each component of our presented strategy. The code is available at: https://github.com/alex-stenger/GUDA.