ThreshGuide: Class-Aware Labeled-Guided Thresholding for Semi-Supervised 3D Abdominal Multi-Organ Segmentation

2026-09-13

AI Summary

为解决半监督3D腹部多器官分割中固定阈值不适用问题，提出ThreshGuide框架，利用标记数据指导未标记数据的伪标签选择。

Abstract

Pseudo-labeling is a strong paradigm for semi-supervised medical image segmentation, yet its effectiveness is highly sensitive to confidence thresholding. In abdominal multi-organ segmentation, a fixed global threshold is particularly suboptimal because organ classes differ substantially in size, appearance, and learning difficulty. In this work, we propose ThreshGuide, a class-aware threshold adaptation framework that uses labeled data to guide pseudo-label selection on unlabeled data. Built upon a standard teacher-student architecture, the teacher model evaluates labeled samples during training to estimate class-aware threshold targets by maximizing an error-aware F\b{eta} criterion that balances precision and coverage. These targets are then smoothed with an exponential moving average (EMA) and used to filter unlabeled voxels in a class-dependent manner. Experiments on FLARE2022 and AMOS2022 show that ThreshGuide performs competitively overall, yielding clear improvements specifically on hard-to-learn organs.

Problem

• semi-supervised
• 3D abdominal multi-organ segmentation
• pseudo-labeling
• confidence thresholding
• organ classes

Innovation

• Class-Aware Thresholding
• Pseudo-labeling
• Semi-supervised Learning
• Error-aware F_beta Criterion