Chidinma P Anakwenze

Aug 21, 2024 | digitalcommons.library.tmc.edu | Cenji Yu |Yao Zhao |Rachael Martin |Chidinma P Anakwenze

AbstractManually delineating upper abdominal organs at risk (OARs) is a time-consuming task. To develop a deep-learning-based tool for accurate and robust auto-segmentation of these OARs, forty pancreatic cancer patients with contrast-enhanced breath-hold computed tomographic (CT) images were selected. We trained a three-dimensional (3D) U-Net ensemble that automatically segments all organ contours concurrently with the self-configuring nnU-Net framework.