Source-free Unsupervised Adaptive Segmentation for Knee Joint MRI

Knee osteoarthritis is a prevalent disease worldwide. The automatic segmentation of knee tissues in magnetic resonance (MR) images has important clinical utility in assessing knee osteoarthritis. Deep learning- based methods show great potential in this application, but they often require a large amount of labeled training data, which is challenging and expensive to acquire. Unsupervised domain adaptation that transfers the learned knowledge from a source labeled dataset to a target unlabeled dataset can be used to address this problem. However, in medical scenarios, domain adaption techniques are often limited by access to the source data due to concerns about patient privacy. In this work, we proposed a novel and effective source-free unsupervised domain adaptation method for knee joint multi-tissue segmentation that does not require a source dataset. The proposed framework is split into two stages. In the first stage, matching batch normalization statistics guides the first segmentation network to realize model adaptation and is combined with augmented entropy minimization to obtain pseudo segmentation labels for the target MR images. The pseudo labels generated by the first segmentation network are then refined using a voting strategy to supervise the training of the models in the second stage. In the second stage, uncertainty-aware cross pseudo supervision is used to further boost the performance of the desired segmentation network, which comprises an encoder and a primary decoder. Our experiments demonstrated that the proposed method outperforms the current state-of-the-art source-free unsupervised domain adaptation techniques for segmenting knee tissues in MR images. Our study also demonstrated that the proposed approach is effective in reverse-directional adaptation.