Few-shot method improves brain tumor segmentation on mri scans
FSS-UBrain: Multi-region Few-Shot Brain Tumor MRI Segmentation
Computer Vision and Pattern Recognition
Summary
Segmenting different parts of brain tumors on MRI scans is hard, especially when only a few labeled examples are available. The authors propose FSS-UBrain, a method that uses just one labeled slice to guide segmentation of specific tumor regions in new scans. Their technique works well across different patient groups without extra fine-tuning. Tests show it can accurately identify whole tumors and tumor cores with fewer errors than other few-shot methods.
What this means in practice
- •For medical imaging software teams: Incorporate few-shot segmentation to enable faster tumor outlining using minimal labeled data from new patient cohorts without retraining.
- •For radiology workflow designers: Implement region-specific segmentation tools that adapt quickly to different scanner settings and populations to assist diagnosis and treatment planning.
Authors
Truong Viet Vu, Nguyen Phuc Nguyen, Dang Thi Thu Hang, Tran Thien Thanh, Vo Nguyen Quoc Bao, Nguyen Thai Anh, Ngo Hoang Tu
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.