Dopnet: Densely Oriented Pooling Network for medical image segmentation

Conference item : Poster

Since manual annotation of medical images is time consuming for clinical experts, reliable automatic segmentation would be the ideal way to handle large medical datasets. Deep learning-based models have been the dominant approach, achieving remarkable performance on various medical segmentation tasks. There can be a significant variation in the size of the feature being segmented out of a medical image relative to the other features in the image, which can be challenging. In this paper, we propose a Densely Oriented Pooling Network (DOPNet) to capture variation in feature size in medical images and preserve spatial interconnection. DOPNet is based on two interdependent ideas: the dense connectivity and the pooling oriented layer. When tested on three publicly available medical image segmentation datasets, the proposed model achieves leading performance.

Authors: Gridach, M; Voiculescu, I

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
• Publication date: 2021-05-25
• Acceptance date: 2021-01-08
• Event title: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
• Event location: Virtual event
• Event website: https://biomedicalimaging.org/2021/
• Event start date: 2021-04-13
• Event end date: 2021-04-16
• DOI: 10.1109/ISBI48211.2021.9434072
• EISSN: 1945-8452
• ISSN: 1945-7928
• EISBN: 978-1-6654-1246-9
• ISBN: 978-1-6654-2947-4
• Language: English
• Keywords: medical image segmentation; deep learning; Densely Oriented Pooling network; FFR
• Subtype: Poster