Thomas Buddenkotte, Leonardo Rundo, Ramona Woitek, Lorena Escudero Sanchez, Lucian Beer, Mireia Crispin-Ortuzar, Christian Etmann, Subhadip Mukherjee, Vlad Bura, Cathal McCague, Hilal Sahin, Roxana Pintican, Marta Zerunian, Iris Allajbeu, Naveena Singh, Anju Sahdev, Laura Havrilesky, David E Cohn, Nicholas W Bateman, Thomas P Conrads, Kathleen M Darcy, G Larry Maxwell, John B Freymann, Ozan Öktem, James D Brenton, Evis Sala, Carola-Bibiane Schönlieb
PURPOSE: To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. METHODS: A deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established "no-new-Net" framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (n = 276), evaluation (n = 104) and testing (n = 71) of the methods...
December 7, 2023: European Radiology Experimental