Abstract
3D pulmonary embolism (PE) segmentation enables quantitative assessment of thrombus burden in computed tomography pulmonary angiography (CTPA), yet public datasets offer only limited cases with voxel-level annotations. We propose PE-QUEST, a quality-guided pseudo-label filtering strategy for annotation-efficient semi-supervised 3D PE segmentation. Pseudo-labels generated from limited annotations are inherently noisy, and conventional confidence-based filtering fails to detect confident yet inaccurate predictions. PE-QUEST addresses this by training an independent quality prediction model that provides an external, image-aware reliability estimate for each pseudo-label. The quality model achieves an MAE of 0.0429 on the private test set and transfers zero-shot across domains without fine-tuning (AUC > 0.93 on public datasets). In semi-supervised experiments with only 20 labeled cases, joint quality-and-uncertainty filtering retains 55% of pseudo-labels and matches fully supervised performance (DSC 0.580 vs. 0.567, NSD 0.712 vs. 0.698). Code is available at https://github.com/iblueer777/ PE-QUEST.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/TIA_003.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{SunQih_PEQUEST_MICCAISAT2026,
author = { Sun, Qihang AND Jian, Bailiang AND Liu, Zhongxiao AND Li, Yitong AND Wachinger, Christian},
title = { { PE-QUEST: QUality ESTimation-driven Sample Selection for Pulmonary Embolism Segmentation } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17269},
month = {pending},
page = {pending}
}