Automated pediatric brain tumor imaging assessment tool from CBTN: Enhancing suprasellar region inclusion and managing limited data with deep learning.

ABSTRACT:

Background

Fully automatic skull-stripping and tumor segmentation are crucial for monitoring pediatric brain tumors (PBT). Current methods, however, often lack generalizability, particularly for rare tumors in the sellar/suprasellar regions and when applied to real-world clinical data in limited data scenarios. To address these challenges, we propose AI-driven techniques for skull-stripping and tumor segmentation.

Methods

Multi-institutional, multi-parametric MRI scans from 527 pediatric patients (n = 336 for skull-stripping, n = 489 for tumor segmentation) with various PBT histologies were processed to train separate nnU-Net-based deep learning models for skull-stripping, whole tumor (WT), and enhancing tumor (ET) segmentation. These models utilized single (T2

SUBMITTER: Gandhi DB 

PROVIDER: S-EPMC11664259 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature