eess.IVAug 25, 2026

Unified-protocol voxel-level pulmonary embolism annotations for three public CT angiography datasets

Authors: Qihang Sun, Zhongxiao Liu, Bailiang Jian, Shenman Qiu, Jingyuan Wang, Lei Zhang, Lixiang Xie, Jiazhen Pan, +1 more

Organizations: Technical University of Munich (TUM), Munich, Germany · Munich Center for Machine Learning (MCML), Munich, Germany · Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China · Department of Radiology, The Third Affiliated Hospital of Soochow University, Changzhou, China · Department of Radiology, The Affiliated Taizhou People’s Hospital of Nanjing Medical University, Taizhou, China

Abstract

Reliable clot-volume quantification and subsequent risk assessment in pulmonary embolism depend on precise segmentation of emboli on computed tomography pulmonary angiography. Deep learning models for this task must be trained on accurate voxel-level labels. The three public datasets that provide such labels were annotated under different protocols, and some of their studies contain unlabeled emboli or labels that are discontinuous across slices. This Data Descriptor presents voxel-level pulmonary embolism annotations for 149 of the 166 studies in these datasets. A primary rater drew all annotations under a single protocol. A thoracic radiologist with more than 20 years of experience reviewed and revised them. Three raters at three different centers independently annotated a subset of 15 studies. The subset was selected by source dataset and embolus location. Technical validation quantifies volumetric agreement with the source annotations, changes in within-mask attenuation, and inter-rater agreement on the subset. The dataset is intended to allow segmentation models to be developed and compared under a common reference standard.

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