Robust AI-Driven Segmentation of Glioblastoma T1c and FLAIR MRI Series and the Low Variability of the MRIMath© Smart Manual Contouring Platform.

Yassine Barhoumi¹, Abdul Hamid Fattah¹, Nidhal Bouaynaya², Fanny Moron³, Jinsuh Kim⁴, Hassan M Fathallah-Shaykh⁵, Rouba A Chahine⁶, Houman Sotoudeh⁵

¹MRIMath, 3473 Birchwood Lane, Birmingham, AL 35243, USA.

²Department of Electrical and Computer Science, Rowan University, Glassboro, NJ 08028, USA.

³Department of Radiology, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA.

⁴Department of Radiology, Emory University, 100 Woodruff Circle, Atlanta, GA 30322, USA.

⁵Department of Neurology, University of Alabama at Birmingham, 510 20th Street South, Birmingham, AL 35294, USA.

⁶RTI International, Durham, NC 27709, USA.

PubMed PMC DOI PDF

Abstract

Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and fluid attenuation inversion recovery (FLAIR) images by comparing their contours to those of three neur…

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