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arXiv · 2508.16934

Addressing Annotation Scarcity in Hyperspectral Brain Image Segmentation with Unsupervised Domain Adaptation

Tim Mach·Daniel Rueckert·Alex Berger·Laurin Lux·Ivan Ezhov
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Abstract

This work presents a novel deep learning framework for segmenting cerebral vasculature in hyperspectral brain images. We address the critical challenge of severe label scarcity, which impedes conventional supervised training. Our approach utilizes a novel unsupervised domain adaptation methodology, using a small, expert-annotated ground truth alongside unlabeled data. Quantitative and qualitative evaluations confirm that our method significantly outperforms existing state-of-the-art approaches, demonstrating the efficacy of domain adaptation for label-scarce biomedical imaging tasks.

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Tim Mach, Daniel Rueckert, Alex Berger, Laurin Lux, Ivan Ezhov. 2025-08-23. Addressing Annotation Scarcity in Hyperspectral Brain Image Segmentation with Unsupervised Domain Adaptation. https://arxiv.org/abs/2508.16934

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