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.

翻译：脑肿瘤是儿童最常见的实体肿瘤，也是儿童癌症相关死亡的首要原因。肿瘤分割在手术与治疗方案制定、疗效评估及监测中至关重要。然而，手动分割耗时且存在较高的操作者间变异性，亟需更高效的方法。我们基于多参数MRI扫描，使用儿童特异性多机构脑肿瘤数据训练后，比较了两种基于深度学习的3D分割模型——DeepMedic与nnU-Net。对339例儿童患者（内部队列n=293，外部队列n=46）的多种肿瘤亚型进行多参数术前MRI扫描预处理，并将肿瘤手动分割为四个子区域：强化肿瘤（ET）、非强化肿瘤（NET）、囊性成分（CC）和瘤周水肿（ED）。训练后，以参考手动分割为基准，采用Dice分数、灵敏度和豪斯多夫距离评估两个模型在内部和外部测试集上的性能。nnU-Net内部测试集的Dice分数（均值±标准差（中位数））为：整体肿瘤（WT）0.9±0.07（0.94），ET 0.77±0.29，NET 0.66±0.32，CC 0.71±0.33，ED 0.71±0.40。DeepMedic的Dice分数分别为：WT 0.82±0.16，ET 0.66±0.32，NET 0.48±0.27，CC 0.48±0.36，ED 0.19±0.33。nnU-Net的Dice分数显著更高（p≤0.01）。在基于多机构BraTS-PEDs 2023数据集的外部验证中，训练后的nnU-Net模型展现出高泛化能力，整体肿瘤和肿瘤核心的分割Dice分数分别为0.87±0.13（0.91）和0.83±0.18（0.89）。基于儿童特异性数据训练的nnU-Net模型在儿童脑肿瘤的整体肿瘤及子区域分割中优于DeepMedic。