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

2025-08-23

AI Summary

To address the severe scarcity of expert annotations in hyperspectral brain image cerebrovascular segmentation, this paper proposes an unsupervised domain adaptation (UDA) deep learning framework tailored for few-shot scenarios. The method leverages a small set of labeled source-domain data (hyperspectral images) and abundant unlabeled target-domain data (same-modality but distributionally heterogeneous images), enabling cross-domain knowledge transfer via adversarial feature distribution alignment and consistency regularization. Its key innovation lies in being the first to systematically integrate UDA into hyperspectral brain image segmentation, substantially reducing annotation dependency. Experiments on a real-world hyperspectral brain dataset demonstrate significant improvements over state-of-the-art methods: +8.2% in Dice coefficient and −3.7 mm in 95th-percentile Hausdorff distance (HD95). Qualitative results further confirm enhanced boundary accuracy and robustness.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Addressing severe label scarcity in brain image segmentation
• Segmenting cerebral vasculature in hyperspectral brain images
• Utilizing unsupervised domain adaptation with limited annotations

Innovation

• Unsupervised domain adaptation for segmentation
• Utilizing expert-annotated and unlabeled data
• Novel deep learning framework for vasculature