Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
This addresses the challenge of adapting medical imaging models across different scanners and protocols without manual labeling, which is incremental as it builds on existing adversarial methods.
The paper tackled the problem of performance degradation in brain lesion segmentation when applying models to new imaging data without annotations, using unsupervised domain adaptation with adversarial networks to achieve segmentation accuracies close to supervised upper bounds.
Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performance of these systems often degrades when they are applied on new data that differ from the training data, for example, due to variations in imaging protocols. Manually annotating new data for each test domain is not a feasible solution. In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain. Specifically, we learn domain-invariant features by learning to counter an adversarial network, which attempts to classify the domain of the input data by observing the activations of the segmentation network. Furthermore, we propose a multi-connected domain discriminator for improved adversarial training. Our system is evaluated using two MR databases of subjects with traumatic brain injuries, acquired using different scanners and imaging protocols. Using our unsupervised approach, we obtain segmentation accuracies which are close to the upper bound of supervised domain adaptation.