Deep Learning-based Stroke Region Segmentation on Susceptibility Weighted Images in Acute Stroke
Ankit Kandpal1, Tanuja Jayas1, Rupsa Bhattacharjee1, Rakesh Kumar Gupta2, and Anup Singh1,3,4
1Centre for Biomedical Engineering, Indian Institute of Technology, Delhi, New Delhi, India, 2Department of Radiology, Fortis Memorial Research Institute, Gurugram, India, 3Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India, 4School for Artificial Intelligence, Indian Institute of Technology, Delhi, New Delhi, India
SWI plays a critical role in stroke in demonstration of hemorrhagic transformation of stroke and demonstration of thrombus in the intracranial arteries. Recently it has been used to quantify the penumbra in acute stroke. It highlights venous vasculature in acute stroke due to hypoxia in the acute ischemic tissue without the need for any contrast injection and adding additional sequence that results in time penalty. The objective of this study was to develop an automatic framework for penumbra detection using only SWI images. Evaluation of segmentation results shows a dice similarity coefficient of 0.72 and a jaccard index of 0.60.
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