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Generalizing a deep learning model to new domains is crucial for computer-aided medical diagnosis systems. Most existing unsupervised domain adaptation methods have made significant progress in reducing the domain distribution gap through adversarial training. However, these methods may still produce overconfident but erroneous results on unseen target images. This paper proposes a new unsupervised domain adaptation framework for cross-modality medical image segmentation. Specifically, We first introduce two data augmentation approaches to generate two sets of semantics-preserving augmented images. Based on the model's predictive consistency on these two sets of augmented images, we identify reliable and unreliable pixels. We then perform a selective entropy constraint: we minimize the entropy of reliable pixels to increase their confidence while maximizing the entropy of unreliable pixels to reduce their confidence. Based on the identified reliable and unreliable pixels, we further propose an adaptive semantic alignment module which performs class-level distribution adaptation by minimizing the distance between same class prototypes between domains, where unreliable pixels are removed to derive more accurate prototypes. We have conducted extensive experiments on the cross-modality cardiac structure segmentation task. The experimental results show that the proposed method significantly outperforms the state-of-the-art comparison algorithms. Our code and data are available at https://github.com/fengweie/SE_ASA.

Rabbit hemorrhagic disease (RHD) is known as rabbit plague and hemorrhagic pneumonia. It is an acute, septic, and highly fatal infectious disease caused by the Lagovirus rabbit hemorrhagic disease virus (RHDV) in the family Caliciviridae that infects wild and domestic rabbits and hares (lagomorphs). At present, RHDV2 has caused huge economic losses to the commercial rabbit trade and led to a decline in the number of wild lagomorphs worldwide. We performed a necropsy and pathological observations on five dead rabbits on a rabbit farm in Tai’an, China. The results were highly similar to the clinical and pathological changes of typical RHD. RHDV2 strain was isolated and identified by RT-PCR, and partial gene sequencing and genetic evolution analysis were carried out. There were significant differences in genetic characteristics and antigenicity between RHDV2 and classical RHDV strain, and the vaccine prepared with the RHDV strain cannot effectively prevent rabbit infection with RHDV2. Therefore, we evaluated the protective efficacy of a novel rabbit hemorrhagic virus baculovirus vector inactivated vaccine (VP60) in clinical application by animal regression experiment. The result showed that VP60 could effectively induce humoral immunity in rabbits. The vaccine itself had no significant effect on the health status of rabbits. This study suggested that the clinical application of VP60 may provide new ideas for preventing the spread of RHD2.

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