mlsanityData to 14 Oct 2026

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

arXiv 2609.14943 ↗First posted 14 Sep 2026cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

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 Fbeta 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 14 Sep 2026.

Builds on

9 of 16 references.

References · 9

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
  3. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  4. 2021Semi-Supervised Semantic Segmentation with Cross Pseudo Supervisioncited by 463
  5. 2022AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentationcited by 351
  6. 2022FreeMatch: Self-adaptive Thresholding for Semi-supervised Learningcited by 232
  7. 2023SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learningcited by 133
  8. 2023Geometric Visual Similarity Learning in 3D Medical Image Self-supervised Pre-trainingcited by 37
  9. 2025DyCON: Dynamic Uncertainty-aware Consistency and Contrastive Learning for Semi-supervised Medical Image Segmentationcited by 8

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2022MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery
  2. 2024Label-efficient multi-organ segmentation with a diffusion model
  3. 2022Knowledge Distillation from Cross Teaching Teachers for Efficient Semi-Supervised Abdominal Organ Segmentation in CT
  4. 2024Data Augmentation for Surgical Scene Segmentation with Anatomy-Aware Diffusion Models
  5. 2021MS-KD: Multi-Organ Segmentation with Multiple Binary-Labeled Datasets
  6. 2026Unsupervised Anatomical Feature Learning via Diffusion Models: Enhanced Medical Image Segmentation with Denoising Diffusion Probabilistic Models
  7. 2025JanusNet: Hierarchical Slice-Block Shuffle and Displacement for Semi-Supervised 3D Multi-Organ Segmentation
  8. 2025Adaptive Knowledge Transferring with Switching Dual-Student Framework for Semi-Supervised Medical Image Segmentation