  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.
