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

Journal: European journal of radiology
Published Date: Jun 25, 2024

Abstract

OBJECTIVES: 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.

Authors

  • Ezio Lanza
    Humanitas University, Department of Biomedical Sciences, Via Rita Levi Montalcini, 4, Pieve Emanuele MI 20072, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy. Electronic address: ezio.lanza@humanitas.it.
  • Angela Ammirabile
    Humanitas University, Department of Biomedical Sciences, Via Rita Levi Montalcini, 4, Pieve Emanuele MI 20072, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy.
  • Marco Francone
    Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Policlinico Umberto I, V.le Regina Elena 324, 00161, Rome, Italy.

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