FSS-UBrain: Multi-region Few-Shot Brain Tumor MRI Segmentation

2026-09-26

AI Summary

This study addresses the challenges of annotation scarcity, cross-cohort variability, and target sparsity in brain tumor MRI segmentation by proposing a region-wise one-shot segmentation framework. Built upon a 2D support-query architecture, the method leverages support slices to guide query feature adaptation and boundary refinement. It further introduces bottleneck interaction and decoder reconstruction mechanisms, while incorporating hard negative mining and empty query regularization to suppress spurious activations and optimize volumetric reconstruction during inference. Experimental results demonstrate that the proposed approach achieves a Whole Tumor Dice score of 89.82% on the BraTS 2020 dataset, exhibiting significantly superior cross-cohort generalization performance compared to existing methods.

Abstract

Accurate delineation of whole tumor (WT), tumor core (TC), and enhancing tumor (ET) from multimodal magnetic resonance imaging remains challenging under limited annotation, cross-cohort variation, and severe target sparsity. We propose FSS-UBrain, a region-wise one-shot segmentation framework that uses a labeled positive support slice to condition binary query segmentation separately for WT, TC, and ET. Support-derived foreground and background descriptors guide query-feature adaptation, bottleneck interaction, decoder-side reconstruction, and boundary refinement. Episodic training additionally incorporates hard-negative and fully negative queries with empty-query regularization to suppress spurious foreground activation when the selected region is absent. Although inference operates on two-dimensional support--query slice pairs, checkpoint selection, threshold calibration, and final evaluation are performed after volumetric reconstruction. FSS-UBrain is evaluated on a held-out BraTS 2020 split and under target-supported cross-cohort protocols on BraTS 2023 and BraTS-Africa. Cases used as target support are excluded from the query cohorts, and no target-domain fine-tuning or test-time parameter updates are performed. On BraTS 2020, FSS-UBrain achieves volumetric Dice scores of 89.82%, 82.14%, and 77.42% for WT, TC, and ET, respectively, with corresponding 95th-percentile Hausdorff distance (HD95) values of 11.12, 9.01, and 4.46 mm. It also achieves the highest mean Dice and lowest finite-pair mean HD95 point estimates on BraTS 2023 and BraTS-Africa among the compared few-shot methods. These findings support target-conditioned few-shot segmentation while highlighting sensitivity to support selection and cohort-specific variation.

Problem

• Few-Shot Segmentation
• Brain Tumor
• MRI
• Cross-cohort Variation
• Target Sparsity

Innovation

• Few-Shot Segmentation
• Brain Tumor MRI
• One-Shot Learning
• Episodic Training
• Cross-Cohort Generalization