Researchers have identified significant limitations in current pulmonary embolism (PE) segmentation algorithms, citing issues with small datasets, lack of reproducibility, and insufficient comparative evaluations. Their study, which involved curating a new dataset of 490 CTPA scans and evaluating nine segmentation architectures, found that 3D U-Net models with ResNet encoding blocks performed best. The paper highlights that distal emboli remain particularly challenging due to task complexity and data scarcity, and makes the best-performing model's architecture and weights publicly available to foster reproducibility. AI
影响 Provides a reproducible baseline and open-weight model for PE segmentation, addressing data scarcity and model evaluation challenges.
排序理由 Academic paper with an open-weight model release and new dataset.
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