Brain tumors are the most common solid tumors and the leading cause of
cancer-related death among children. Tumor segmentation is essential in
surgical and treatment planning, and response assessment and monitoring.
However, manual segmentation is time-consuming and has high inter-operator
variability, underscoring the need for more efficient methods. We compared two
deep learning-based 3D segmentation models, DeepMedic and nnU-Net, after
training with pediatric-specific multi-institutional brain tumor data using
based on multi-parametric MRI scans.Multi-parametric preoperative MRI scans of
339 pediatric patients (n=293 internal and n=46 external cohorts) with a
variety of tumor subtypes, were preprocessed and manually segmented into four
tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic
components (CC), and peritumoral edema (ED). After training, performance of the
two models on internal and external test sets was evaluated using Dice scores,
sensitivity, and Hausdorff distance with reference to ground truth manual
segmentations. Dice score for nnU-Net internal test sets was (mean +/- SD
(median)) 0.9+/-0.07 (0.94) for WT, 0.77+/-0.29 for ET, 0.66+/-0.32 for NET,
0.71+/-0.33 for CC, and 0.71+/-0.40 for ED, respectively. For DeepMedic the
Dice scores were 0.82+/-0.16 for WT, 0.66+/-0.32 for ET, 0.48+/-0.27, for NET,
0.48+/-0.36 for CC, and 0.19+/-0.33 for ED, respectively. Dice scores were
significantly higher for nnU-Net (p<=0.01). External validation of the trained
nnU-Net model on the multi-institutional BraTS-PEDs 2023 dataset revealed high
generalization capability in segmentation of whole tumor and tumor core with
Dice scores of 0.87+/-0.13 (0.91) and 0.83+/-0.18 (0.89), respectively.
Pediatric-specific data trained nnU-Net model is superior to DeepMedic for
whole tumor and subregion segmentation of pediatric brain tumors.

通过多机构儿童专用多参 MRI 扫描数据训练，我们比较了两种基于深度学习的三维分割模型，即 DeepMedic 和 nnU-Net，在脑肿瘤分割方面的性能，结果显示儿童专用数据训练的 nnU-Net 模型在整个肿瘤及亚区域分割方面优于 DeepMedic。