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Abstract #1778

Large Blood Vessel Segmentation using Swin Transformers for Improved Quantitative DCE-MRI of Gliomas

Anshika Kesari1, Satyajit Maurya1, Mohammad Tufail Sheikh1, 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 Delhi, New Delhi, India, 4Yardi School of Artificial Intelligence, IIT Delhi, New Delhi, India

Synopsis

Keywords: Blood Vessels, Blood vessels

Motivation: Quantitative DCE-MRI parameters provide a noninvasive way to evaluate tumor microvasculature, crucial for tumor assessment. However, the presence of normal large-blood-vessels(LBVs) within tumor regions can interfere with accurate tumor evaluation.

Goal(s): To develop a transformer based LBV segmentation method aimed to enhance tumor grading accuracy and treatment planning in glioma patients.

Approach: To generate the ground truth LBV masks, k-means clustering was applied to combined DCE-MRI parameters (CBV and Slope-2 maps), with manual corrections as needed. The performance of Swin UNETR was compared against U-Net for LBV segmentation.

Results: Swin UNETR model outperformed the standard U-Net, demonstrating superior performance in LBV segmentation.

Impact: The proposed transformer based LBV segmentation algorithm can aid radiologists in achieving more objective and accurate tumor assessment, potentially overcoming limitations associated with manual or semi-automatic segmentation techniques.

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Keywords