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Projection network with Spatio-temporal information: 2D + time DSA to 2D aorta segmentation

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Abstract

Aortic dissection (AD) is an acute cardiovascular disease with high mortality and disability rates. AD is commonly treated by the thoracic endovascular aortic repair(TEVAR) which relies on the aorta’s position in the Digital Subtraction Angiography (DSA). However, patients and doctors need to be constantly exposed to the X-rays during the DSA process. Besides, the aorta is partially displayed in each frame with blurred boundaries and inhomogeneously distributed contrast agent. The accurate segmentation of AD in DSA is essential for stent placement. This paper proposes a projection network with spatio-temporal information (PNet-ST) for the aortic segmentation of DSA. We introduce a spatial encoder to learn the partial aortic structure information in each frame. Meanwhile, the max intensity projection (MIP) skip connections are used to fuse the temporal information preserved by the encoder to obtain the complete aortic structure. Furthermore, the dense biased connections integrate the multi-receptive field to enhance the network’s sensitivity for the multi-resolution feature. The experiment results show that our PNet-ST with segmentation DSC of 0.897, Precision of 0.8757, Recall of 0.9202 and Acc of 0.9684, outperforming the previous image segmentation techniques, such as CE-Net, HRNET and U-Net. The segmentation results of our PNet-ST can offer assistance in endovascular surgery and help the doctors observe the entire aorta to place the stent more accurately.

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Funding

This work was supported in part by the National Science and Technology Major Project of the Ministry of Science and Technology of China under Grant 2018ZX10201002–003 and National Natural Science Foundation under Grant 6187111.

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Authors and Affiliations

  1. Laboratory of Image Science and Technology, Key Laboratory of Computer Network and Information Integration, School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China

    Weiya Sun, Yuting He, Guanyu Yang, Yang Chen & Huazhong Shu

  2. Centre de Recherche en Information BioMdicale Sino-franais (CRIBs), Nanjing, China

    Weiya Sun, Yuting He, Guanyu Yang, Yang Chen & Huazhong Shu

  3. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Rongjun Ge

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  1. Weiya Sun
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  2. Yuting He
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Correspondence to Huazhong Shu.

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Sun, W., He, Y., Ge, R. et al. Projection network with Spatio-temporal information: 2D + time DSA to 2D aorta segmentation. Multimed Tools Appl 81, 28021–28035 (2022). https://doi.org/10.1007/s11042-022-12117-6

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  • DOI: https://doi.org/10.1007/s11042-022-12117-6

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