Abstract #1018

Deep Learning-based Stroke Region Segmentation on Susceptibility Weighted Images in Acute Stroke

Ankit Kandpal¹, Tanuja Jayas¹, Rupsa Bhattacharjee¹, Rakesh Kumar Gupta², and Anup Singh^1,3,4

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

Synopsis

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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.