用于儿童脑肿瘤自动分割的nnU-Net与DeepMedic方法的训练及比较
Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors
AI总结：
针对儿童脑肿瘤手动分割效率低、差异大的问题，研究用儿童多机构MRI数据训练DeepMedic与nnU-Net模型，发现nnU-Net分割性能更优且泛化能力强。
摘要：
脑肿瘤是儿童群体中最常见的实体瘤，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规划、疗效评估及监测中至关重要，但手动分割耗时久，且操作者间差异大，因此亟需更高效的分割方法。我们使用儿童专属的多机构脑肿瘤数据，基于多参数MRI扫描训练了两种基于深度学习的3D分割模型——DeepMedic和nnU-Net，并对二者进行比较。\n研究对339名患有不同肿瘤亚型的儿童患者（内部队列n=293，外部队列n=46）的术前多参数MRI扫描进行预处理，并由人工分割出四个肿瘤子区域，即强化肿瘤（ET）、非强化肿瘤（NET）、囊性成分（CC）和瘤周水肿（ED）。训练完成后，以人工分割的金标准为参照，采用Dice系数、灵敏度和豪斯多夫距离，评估两个模型在内部及外部测试集上的性能。\nnnU-Net在内部测试集上的Dice系数（均值±标准差（中位数））分别为：全肿瘤（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系数分别为：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系数显著更高（p≤0.01）。将训练好的nnU-Net模型在多机构BraTS-PEDs 2023数据集上进行外部验证，结果显示其在全肿瘤和肿瘤核心分割方面具有高泛化能力，Dice系数分别为0.87±0.13（0.91）和0.83±0.18（0.89）。经儿童专属数据训练的nnU-Net模型在儿童脑肿瘤的全肿瘤及子区域分割任务中，性能优于DeepMedic。
英文摘要：
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.
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