Keywords: Blood Vessels, Blood vessels, Clustering algorithms, Brain tumor
Motivation: The presence of normal large-blood-vessels(LBV) in tumor region can impact the evaluation of quantitative DCE-MRI parameters and tumor classification.
Goal(s): To develop an automated framework for segmenting LBVs present within or around the tumor region using different clustering algorithms and compare their accuracy in tumor grading.
Approach: LBV masks were generated using three different clustering algorithms on the DCE-MRI derived maps CBV and Slope-2. Generated tumor mask using AI tool on FLAIR images. Statistical analysis was performed.
Results: Overall, k-means clustering based algorithm provided superior performance in segmentation of LBV and tumor grading in less computational time.
Impact: The proposed automatic LBV segmentation algorithm can assist radiologists in objective and accurate assessment of tumor including tumor grading. This will reduce errors in tumor assessment.
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