Domain Adaptation for MRI Organ Segmentation using Reverse Classification Accuracy
This addresses the costly manual annotation issue in medical imaging for domain adaptation, but it is incremental as it builds on existing RCA concepts.
The paper tackled the problem of performance degradation in MRI organ segmentation when models are applied to new scanners or sequences, by using reverse classification accuracy to automatically select subjects for annotation, reducing annotation burden.
The variations in multi-center data in medical imaging studies have brought the necessity of domain adaptation. Despite the advancement of machine learning in automatic segmentation, performance often degrades when algorithms are applied on new data acquired from different scanners or sequences than the training data. Manual annotation is costly and time consuming if it has to be carried out for every new target domain. In this work, we investigate automatic selection of suitable subjects to be annotated for supervised domain adaptation using the concept of reverse classification accuracy (RCA). RCA predicts the performance of a trained model on data from the new domain and different strategies of selecting subjects to be included in the adaptation via transfer learning are evaluated. We perform experiments on a two-center MR database for the task of organ segmentation. We show that subject selection via RCA can reduce the burden of annotation of new data for the target domain.