Motivation: Automatic volumetric approach for accurate glioblastoma segmentation combined with perfusion and diffusion data offers significant advantages for quantitative image analysis over two-dimensional manual methods commonly used, enabling precision medicine, improved prognostication, treatment planning, and follow-up. Goal(s): Develop and implement an automated pipeline for multi-compartmental glioblastoma segmentation and physiologic MRI parameter extraction. Approach: Utilizing our validated deep-learning segmentation algorithm and Olea-Sphere software, we created a pipeline generating coregistered anatomic, diffusion, and perfusion MRI sequences with overlaid segmentation masks and descriptive statistics for sub-compartment volumes visible in PACS. Results: The pipeline outputs comprehensive MRI data with segmented compartments and quantitative metrics. Impact: This automated pipeline can enhance clinical decision-making and personalized treatment for glioblastoma patients. Its development will facilitate new research on imaging biomarkers, ultimately improving patient outcomes and advancing neuroimaging practices.
Lotan et al. (Tue,) studied this question.
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