Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning

TL;DR

This paper presents a deep multitask learning model specifically designed for segmenting pediatric low-grade gliomas in MRI scans, incorporating genetic information to enhance accuracy, addressing the gap in pediatric brain tumor segmentation research.

Contribution

The study introduces a novel deep multitask learning approach that combines tumor segmentation with genetic alteration classification for pediatric gliomas, improving segmentation accuracy.

Findings

Enhanced segmentation accuracy through multitask learning.

Effective integration of genetic data improves model performance.

Addresses the scarcity of pediatric-specific brain tumor segmentation methods.

Abstract

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.