nnU-Net-based deep-learning for pulmonary embolism: detection, clot volume quantification, and severity correlation in the RSPECT dataset

CT pulmonary angiography is the gold standard for diagnosing pulmonary embolism, and DL algorithms are being developed to manage the increase in demand. The nnU-Net is a new auto-adaptive DL framework that minimizes manual tuning, making it easier to develop effective algorithms for medical imaging even without specific expertise. This study assesses the performance of a locally developed nnU-Net algorithm on the RSPECT dataset for PE detection, clot volume measurement, and correlation with right ventricle overload.