Deep-learning Segmentation of Pediatric Brain Tumors using Ratio Maps of T1w/T2w MRI Signal Intensity
Abstract
Introduction
T1w/T2w ratio mapping, combining voxel-wise signal intensities in T1-weighted (T1w) and T2-weighted (T2w) structural MRI, has been used to investigate cortical architecture in the brain, but has also shown promise in tissue discrimination, even in tumor tissue.
Methods
The current study aimed to investigate whether the inclusion of these established T1w/T2w ratio maps, or a similar T1w – T2w combined map, can improve performance on a novel task; automated segmentation of tumor tissue in pediatric brain tumor cases from the BraTS-PED 2024 dataset.
Results
We demonstrate that T1w/T2w ratio maps do not improve deep learning models for segmenting tumor subregions using nnU-Net.
Conclusions
Overall, our results suggest that including well-established combinations of T1 & T2w imaging modalities in addition to the already utilized mpMRI, as a potential method of input data augmentation, does not provide added value in the task of segmentation of pediatric brain tumors.
Summary Statement
The combination of T1w and T2w MRI images of the brain serves as a method to derive additional inputs for deep learning models. Their inclusion does not improve state-of-the art automated segmentation of pediatric brain tumors from standard MRI modalities.
Key Results
• Baseline segmentation differed for different tumor subregions. • Segmentation performance when T1w/T2w ratio maps were included, was similar to baseline for all tumor subregions. • Accuracy of enhancing tumor had greatest increase through inclusion of T1w/T2w Ratio Map but was not a statistically significant improvement.
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