… image segmentation. The goal of this study was to validate a deep learning model for automatic
segmentation … We hypothesized that (1) a deep learning model would segment repaired …

… segmentation method based on U-net and LinkNet deep learning networks combined with
transfer learning … Additionally, we evaluate the model’s ability to learn to segment the disease …

… ent scanners or different imaging protocols presents a major challenge in automatic
segmentation of biomedical images. This variation especially hampers the application of otherwise …

… In this work, we investigated the feasibility of deep learning for the automatic segmentation
of the CTV, bladder, and rectum in prostate cancer patients treated at a 0.35 T MR-Linac. …

… train a 2D model for the task of semantic segmentation. In order to further improve model
convergence, we hypothesized that utilizing transfer learning from a model pre-trained on the …

… However, the segmentation of retinal … for automatic segmentation of three regions bounded
by four retinal boundaries in patients with age-related macular degeneration. Segmentation …

… Recent advances in deep learning have … segmentation. In particular, fully convolutional
networks (FCNs) such as the U-Net produce state-of-the-art results in the automatic segmentation …

… a longitudinal transfer learning paradigm in which the algorithm learns from segmentation
errors … Our method compared favorably to other deep learning-based and non-deep learning-…

… limit the improvement of performance in the transfer learning since the continuing training on
the … To further evaluate the robustness of the transfer learning approach, we are acquiring …

… In this work, we developed an automatic segmentation method of the hippocampus from
magnetic resonance imaging, incorporating a number of different CNNs and exploiting their …