Dhaval Dilip Kadia
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The recent developments in the field of Medical Imaging, Deep Learning, is a crucial technology to accelerate medical tasks and perform them precisely and automatically. 3D lung segmentation has a significant role in removing the unnecessary volume of 3D CT scans and segments the actual volume of the lungs in three dimensions, to simplify the 3D CT scan for further tasks. Recently, the deep learning network such as U-Net and its variants provides excellent results for biomedical image segmentation. We propose a novel deep neural network architecture based on UNet, for the 3D lung segmentation task. The proposed model helps learn spatial dependencies in 3D and increases the propagation of volumetric information. We have investigated our network with different architectural modules, learning strategy, activation functions, optimizers, loss functions, and appropriate hyperparameters. Our proposed deep neural network is trained on the publicly available dataset - LUNA16 and achieves state-of-the-art performance on the VESSEL12 dataset and the testing set of LUNA16.
Van Tam Nguyen, Vijayan K. Asari
Primary Advisor's Department
Stander Symposium project, College of Arts and Sciences
United Nations Sustainable Development Goals
Good Health and Well-Being
"UNet-based Deep Neural Network for 3D Lung Segmentation" (2020). Stander Symposium Projects. 1798.
This presentation was given live via Zoom at at 9:00 a.m. (Eastern Time) on Wednesday, April 22.