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Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning
Partoo Vafaeikia · Matt Wagner · Brigit Ertl-Wagner · Farzad Khalvati

Brain tumor segmentation is a critical task for tumor volumetric analyses and AI algorithms. However, it is a time-consuming process and requires neuroradiology expertise. While there has been extensive research focused on optimizing brain tumor segmentation in the adult population, studies on AI guided pediatric tumor segmentation are scarce. Furthermore, MRI signal characteristics of pediatric and adult brain tumors differ, necessitating the development of segmentation algorithms specifically designed for pediatric brain tumors. We developed a segmentation model trained on magnetic resonance imaging (MRI) of pediatric patients with low-grade gliomas (pLGGs) from our local research hospital. The proposed model utilizes deep Multitask Learning (dMTL) by adding tumor's genetic alteration classifier as an auxiliary task to the main network, ultimately improving the accuracy of the segmentation results.

Author Information

Partoo Vafaeikia (University of Toronto)
Matt Wagner (The Hospital for Sick Children)
Brigit Ertl-Wagner (The Hospital for Sick Children)
Farzad Khalvati (University of Toronto)

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