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

TL;DR

This paper introduces an unsupervised domain adaptation framework that effectively segments cerebral vasculature in hyperspectral brain images despite severe label scarcity, outperforming existing methods.

Contribution

The work presents a novel unsupervised domain adaptation approach tailored for hyperspectral brain image segmentation with limited annotations.

Findings

Significant performance improvement over state-of-the-art methods

Effective handling of label scarcity in biomedical imaging

Validated through comprehensive quantitative and qualitative evaluations

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.