Automated segmentation of pediatric brain tumors based on multi-parametric MRI and deep learning

Abstract

Background: Brain tumors are the most common solid tumors in children, and high-grade brain tumors are the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential for surgical planning, treatment planning, and radiomics studies, but manual segmentation is time-consuming and has high interoperator variability. This paper presents a deep learning-based method for automated segmentation of pediatric brain tumors based on multi-parametric MRI. Methods: Multi-parametric MRI (MP-MRI) scans (T1, T1w-Gd, T2, and FLAIR) of 167 pediatric patients with de novo brain tumors, including a variety of tumor subtypes, were processed and manually segmented according to Response Assessment in Pediatric Neuro-Oncology (RAPNO) guidelines into five tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic core (CC), cystic reactive (CR), and peritumoral edema (ED). Segmentations were revised and approved by experienced neuroradiologists and used as the ground truth (GT). The cohort was split into training (n=134) and independent test (n=33) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained with 7-fold cross-validation, and the model’s parameters were tuned. Finally, the network was evaluated on the withheld test cohort, and its performance was assessed in comparison with GT segmentations. Results: Dice similarity score (mean+/-SD) was 0.71+/-0.28 for the whole tumor (union of all five subregions), 0.66+/- 0.30 for ET, 0.31+/-0.26 for NET, 0.34+/-0.38 for CC, 0.55+/-0.50 for CR, and 0.32+/-0.42 for ED. Conclusion: This model displayed good performance on segmentation of the whole tumor region of pediatric brain tumors and can facilitate detection of abnormal region for further clinical measurements.