  Liver segmentation on images acquired using computed tomography (CT) and
magnetic resonance imaging (MRI) plays an important role in clinical management
of liver diseases. Compared to MRI, CT images of liver are more abundant and
readily available. However, MRI can provide richer quantitative information of
the liver compared to CT. Thus, it is desirable to achieve unsupervised domain
adaptation for transferring the learned knowledge from the source domain
containing labeled CT images to the target domain containing unlabeled MR
images. In this work, we report a novel unsupervised domain adaptation
framework for cross-modality liver segmentation via joint adversarial learning
and self-learning. We propose joint semantic-aware and shape-entropy-aware
adversarial learning with post-situ identification manner to implicitly align
the distribution of task-related features extracted from the target domain with
those from the source domain. In proposed framework, a network is trained with
the above two adversarial losses in an unsupervised manner, and then a mean
completer of pseudo-label generation is employed to produce pseudo-labels to
train the next network (desired model). Additionally, semantic-aware
adversarial learning and two self-learning methods, including pixel-adaptive
mask refinement and student-to-partner learning, are proposed to train the
desired model. To improve the robustness of the desired model, a low-signal
augmentation function is proposed to transform MRI images as the input of the
desired model to handle hard samples. Using the public data sets, our
experiments demonstrated the proposed unsupervised domain adaptation framework
reached four supervised learning methods with a Dice score 0.912 plus or minus
0.037 (mean plus or minus standard deviation).
