Source-Free Active Domain Adaptation for Brain Tumor Segmentation via Mamba and Region-Level Uncertainty.

Overview

1. Department of Electronic Science, Xiamen University, Xiamen 361005, China; (H.Z.); (C.W.); (C.C.)
2. Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China

Abstract

Background/Objectives: Accurate brain tumor segmentation from MRI is crucial for diagnosis but faces challenges like domain shifts across medical centers, data privacy constraints, and high annotation costs. While source-free active domain adaptation (SFADA) emerges as a promising solution to these issues, existing approaches often overlook the inherent structural complexity in tumor regions. Methods: We propose a novel SFADA framework composed of two major contributions. First, we introduce a Region-level Uncertainty-Guided Sample Selection (RUGS) strategy, enabling the identification of the most informative target-domain samples in a single inference pass. Second, we present the Source-Free Active Domain Adaptation Network (SFADA-Net), a Mamba-driven segmentation model equipped with a dual-path multi-kernel convolution module for enhanced local feature interaction and a structure-aware prompted Mamba module for capturing global spatial relationships. Results: Extensive evaluations across one source domain dataset (BraTS-2021) and three target domain datasets (BraTS-SSA, BraTS-PED, and BraTS-MEN 2023) demonstrate the superior adaptability of the proposed method, achieving consistently high segmentation accuracy across domains. With only 5% annotation budget, our framework consistently outperforms state-of-the-art segmentation and domain adaptation methods, achieving robust segmentation accuracy across diverse domains and approaching the performance of fully supervised learning. Conclusions: The proposed method achieves superior accuracy in brain tumor region segmentation and precise boundary delineation under a limited annotation budget. It effectively mitigates domain shift while fully complying with data privacy regulations. Consequently, our framework relieves manual annotation bottlenecks and accelerates the cross-center deployment of accurate diagnostic tools, facilitating the clinical application of domain adaptation.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data Availability Statement

The data that support the findings of this study are publicly available from the BraTS challenge at https://www.synapse.org/ (accessed on 24 February 2025). Detailed information regarding data splits and preprocessing procedures is provided in the Experiments and Results section. The code used in this study is publicly available at: https://github.com/ZHW11/SFADA-for-brain-tumor-segmentation (accessed on 12 February 2026).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 25 references.

Cite

This paper

Zheng, H., Wang, C., Zhou, Y., Cai, C., & Chen, Z. (2026). Source-Free Active Domain Adaptation for Brain Tumor Segmentation via Mamba and Region-Level Uncertainty. Brain sciences, 16(3), 300. https://doi.org/10.3390/brainsci16030300

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