Abstract #0884

Differentiating hemorrhage from tumor vasculature in SWI-MRI images using Swin UNETR: Applications in Glioma Grading

Satyajit Maurya¹, Mohammad Tufail Sheikh¹, Rakesh Kumar Gupta², and Anup Singh^1,3,4

¹Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India, ²Fortis Memorial Research Institute, New Delhi, India, ³Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India, ⁴Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, India

Synopsis

Keywords: Tumors (Pre-Treatment), Blood vessels, Susceptibility weighted imaging

Motivation: Glioma grading using intra-tumoral-susceptibility-signal (ITSS) from SWI is affected by the presence of hemorrhage within the tumor region. Separation of ITSS vasculature (IV) from hemorrhage is challenging.

Goal(s): To develop a deep learning-based method for segmenting IV from SWI.

Approach: Swin UNETR model was trained for separation between IV and hemorrhage components of ITSS tissues on SWI-MRI. Data of 214 Glioma patients was used. The potential of IV in glioma grading was also evaluated.

Results: Swin UNETR provided a dice score of 0.910±0.060 for IV segmentation. The predicted and the ground truth masks showed similar glioma grading accuracies (P<0.001).

Impact: This study presents a method for automatically segmenting IV from SWI images using Swin UNETR without multi-echo SWI or R2* maps, enhancing efficiency in resource-limited settings, showing high accuracy in classifying gliomas. It addresses the subjectivity inherent in manual methods.

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