An end-end deep learning framework for lesion segmentation on multi-contrast MR images—an exploratory study in a rat model of traumatic brain injury-Reference-Cited by-同舟云学术

An end-end deep learning framework for lesion segmentation on multi-contrast MR images—an exploratory study in a rat model of traumatic brain injury

Published:2023-01-10 Issue:3 Volume:61 Page:847-865
ISSN:0140-0118
Container-title:Medical & Biological Engineering & Computing
language:en
Short-container-title:Med Biol Eng Comput

Author:

KN Bhanu Prakash^ORCID,CS Arvind,Mohammed Abdalla,Chitta Krishna Kanth,To Xuan Vinh,Srour Hussein,Nasrallah Fatima

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11517-022-02752-4.pdf

Reference61 articles.

1. Centers for Disease Control and Prevention. (2015). Report to congress on traumatic brain injury in the United States: epidemiology and rehabilitation. National Center for Injury Prevention and Control; Division of Unintentional Injury Prevention. Atlanta, GA

2. Marr, A.L., and Coronado, V.G. (Eds.). (2004). Central nervous system injury surveillance data submission standards—2002. Atlanta, GA: Centers for Disease Control and Prevention, National Center for Injury Prevention and Control.

3. McKee AC, Daneshvar DH (2015) The neuropathology of traumatic brain injury. Handb Clin Neurol 127:45–66

4. Unterberg AW, Stover J, Kress B, Kiening KL (2004) Edema and brain trauma. Neuroscience 129:1021–1029

5. Schweitzer AD, Niogi SN, Whitlow CJ, Tsiouris AJ (2019) Traumatic brain injury imaging patterns and complications. Radiograph: Rev Pub Radiol Soc North Am Inc 39(6):1571–1595

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