Significant advances have been made toward building accurate automatic segmentation models for adult gliomas. However, the performance of these models often degrades when applied to pediatric glioma due to their imaging and clinical differences (domain shift). Obtaining sufficient annotated data for pediatric glioma is typically difficult because of its rare nature. Also, manual annotations are scarce and expensive. In this work, we propose Domain-Adapted nnU-Net (DA-nnUNet) to perform unsupervised domain adaptation from adult glioma (source domain) to pediatric glioma (target domain). Specifically, we add a domain classifier connected with a gradient reversal layer (GRL) to a backbone nnU-Net. Once the classifier reaches a very high accuracy, the GRL is activated with the goal of transferring domain-invariant features from the classifier to the segmentation model while preserving segmentation accuracy on the source domain. The accuracy of the classifier slowly degrades to chance levels. No annotations are used in the target domain. The method is compared to 8 different supervised models using BraTS-Adult glioma (N=1251) and BraTS-PED glioma data (N=99). The proposed method shows notable performance enhancements in the tumor core (TC) region compared to the model that only uses adult data: ~32% better Dice scores and ~20 better 95th percentile Hausdorff distances. Moreover, our unsupervised approach shows no statistically significant difference compared to the practical upper bound model using manual annotations from both datasets in TC region. The code is shared at https://github.com/Fjr9516/DA_nnUNet.

翻译：针对成人胶质瘤的精确自动分割模型已取得显著进展。然而，由于成像与临床特征的差异（域偏移），这些模型应用于儿童胶质瘤时性能常出现下降。鉴于儿童胶质瘤的罕见性，获取足量标注数据通常较为困难，且人工标注稀缺且成本高昂。本研究提出域自适应nnU-Net（DA-nnUNet），实现从成人胶质瘤（源域）到儿童胶质瘤（目标域）的无监督域自适应。具体而言，我们在骨干nnU-Net中连接了带梯度反转层（GRL）的域分类器。当分类器达到较高准确率后激活GRL，旨在将分类器的域不变特征迁移至分割模型，同时保持源域的分割精度。此时分类器准确率逐渐降至随机水平。目标域未使用任何标注数据。该方法在BraTS成人胶质瘤数据集（N=1251）和BraTS-PED儿童胶质瘤数据集（N=99）上，与8种不同监督模型进行对比。在肿瘤核心（TC）区域，所提方法相比仅使用成人数据的模型表现出显著性能提升：Dice分数提高约32%，95%分位数豪斯多夫距离改善约20个单位。此外，在TC区域，本无监督方法与使用双数据集人工标注的实际上界模型相比无统计学显著差异。代码已发布于https://github.com/Fjr9516/DA_nnUNet。