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 this https URL

在本研究中，我们提出了由 Domain-Adapted nnU-Net (DA-nnUNet) 构成的无监督领域自适应模型，用于将成人胶质瘤（源域）应用到儿童胶质瘤（目标域）中，通过添加一个连接有梯度反转层（GRL）的域分类器到 nnU-Net 的骨干网络中实现特征转换，并在保持源域分割准确性的同时将领域无关特征转移到分割模型，结果显示相较于仅使用成人数据的模型，在肿瘤核心（TC）区域有明显性能提升，Dice 系数约提高 32％，95th percentile Hausdorff 距离约提高 20 个单位。