Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning
This work addresses the segmentation of pediatric brain tumors, a critical but understudied area in medical imaging, with incremental improvements for clinical applications.
The researchers tackled the problem of segmenting pediatric low-grade gliomas from MRI scans, which is scarce and challenging due to differences from adult tumors, by developing a deep multitask learning model that includes a genetic alteration classifier as an auxiliary task, resulting in improved segmentation accuracy.
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 The Hospital for Sick Children (Toronto, Ontario, Canada). 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.