3D region-growing nnU-Net improves pulmonary embolism detection on CTPA: a dual-cohort validation study
Our 3D nnU-Net model automates clot detection on CT scans in seconds and shows numerically higher performance than the 2D models. Built on local data, this framework enables institution-specific model training and validation to complement European conformity‒CE-marked tools and assess performance locally. High-sensitivity volumetric quantification reduces missed emboli, paving the way for personalized risk stratification and improved patient outcomes.
Authors
- Alessandra Mininni
- Andrea Laghi
- Angela Ammirabile (ORCID: https://orcid.org/0000-0002-3724-053X)
- Andrea Vanzulli (ORCID: https://orcid.org/0000-0001-6281-7807)
- Marco Francone
- A. Lucia
- Riccardo Levi
- Ezio Lanza
- Arosh Shavinda Perera
- Costanza Lisi
Institutions
- Humanitas University (IT)
- University of Milan (IT)
- Fondazione IRCCS Istituto Nazionale dei Tumori (IT)
- IRCCS Humanitas Research Hospital (IT)
- Sapienza University of Rome (IT)
Publication Details
- Journal
- European Radiology Experimental
- Published
- 2026-03-04
- DOI
- https://doi.org/10.1186/s41747-026-00693-3
- Primary Topic
- Venous Thromboembolism Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministero della Salute
- Humanitas Research Hospital