Work

Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline

Year: 2019

Type: article

Source: Computers in Biology and Medicine

DOI: 10.1016/j.compbiomed.2019.01.018

Authors: Hyeonsoo Moon, Yuankai Huo, Richard G. Abramson, Richard Alan Peters, Albert Assad, Tamara K. Moyo, Michael R. Savona, Bennett A. Landman

Institutions: Vanderbilt University, Vanderbilt-Ingram Cancer Center, Incyte (United States), Vanderbilt Health, Vanderbilt University Medical Center

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources, National Center for Advancing Translational Sciences

Awards: GPU-Accelerated Parallel Computer for Drug Discovery Applications, The Vanderbilt Institute for Clinical and Translational Research (VICTR) UL1, Controlling Quality and Capturing Uncertainty in Advanced Diffusion Weighted MRI, The Vanderbilt Institute for Clinical and Translational Research (VICTR), Network-Attached Biomedical Research Data Management System, Cancer Center Support Grant

OpenAlex ID: W2914393040

FWCI: 1.231

Cites: 25

Cited by: 19

Related to: 10