From adult to pediatric: deep learning-based automatic segmentation of rare pediatric brain tumors

Abstract

Pediatric diffuse midline glioma (DMG) is a rare but fatal pediatric brain tumor. Tumor MRI features, extracted from segmented DMG, have shown promise for predicting DMG progression and overall survival. The data and knowledge accumulated from the more common adult brain tumors cannot be directly applied to DMG due to different tumor locations and appearances. The purpose of this work is to develop a transfer learning-based approach to automatically preprocess and segment sub-regions of DMG from multisequence MRIs. We retrospectively collected T1, contrastenhanced T1, T2 and T2 FLAIR images of 45 children diagnosed with DMG. MRI images at two timepoints were considered: at diagnosis and after completion of radiation therapy (RT). This generated a DMG dataset of 82 cases. Manual segmentation of two labels were created: the enhancing region (ER) and the whole tumor (WT). We modified the SegResNet model developed by NVIDIA and pre-trained it on BraTS 2021 challenge dataset, which contains 1,251 subjects with adult glioblastoma multiforme. DMG data was automatically preprocessed to have the same resolution and format as the input data in the BraTS challenge. A 5-fold cross-validation was performed using the preprocessed DMG data to finetune and validate the model. The proposed method resulted in mean Dice scores of 0.831 and 0.840 for the ER and WT segmentations, respectively. The method produced decent segmentation results for a small dataset. We demonstrated transfer learning from adult brain tumors to rare pediatric brain tumors was feasible and would improve segmentation results.