ABSTRACT
Aortic dissection is a rapid and critical cardiovascular disease. The automatic segmentation and detection of related organs and lesions in CT volumes of aortic dissection provide great help for the rapid diagnosis and treatment of aortic dissection. However, the diagnosis of aortic dissection involves multi-organ and lesion segmentation, which is also a multi-label segmentation problem. It faces many challenges, such as small target scale, variable location of the true and false lumen, and complex judgment. To solve these problems, this paper proposes a deep model (MOLS-Net) to segment and detect aortic dissection from CT volumes quickly and automatically. First, the sequence feature pyramid attention module correlates CT image sequence features of different scales and guides the current image segmentation by exploring the correlation between slices. Secondly, combine the spatial attention module and the channel attention module in the decoder of the network to strengthen the model’s positioning accuracy of the target area and the feature utilization. Thirdly, this paper designs a multi-label classifier for the inter-class relationship of multi-label segmentation of aortic dissection and realizes multi-label segmentation on the end-to-end network. In this paper, we evaluate MOLS-Net on multiple datasets (self-made aortic dissection segmentation dataset and COVID-19 CT segmentation dataset), and the results show that the proposed method is superior to other state-of-the-art methods.