Abstract
Myocardial segmentation plays a pivotal role in the clinical diagnosis of cardiac diseases. The difference in size and shape of the heart poses an extensive challenge to the clinical diagnosis. Being specific, the large amount of noise generated by the cardiac magnetic resonance (CMR) images also gives rise to substantial interference in the clinical diagnosis. Inspired by associated tasks, we put forward a network for the myocardium segmentation. In the proposed methodology, at first, we establish numerous sub-sampling layers in a bid to attain the high-level features, together with fusing the feature information of different visual fields by assuming different convolution kernel sizes. Thereafter, high-level features coupled with initial input features are merged by means of a plurality of cascaded convolution layers. It is capable of directly improving the performance of myocardium segmentation. We perform an assessment of our approach on 165 CMR T1 mapping images with lower PSNR, and the results demonstrate that our architecture outperforms previous approaches.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (Grant No. 61602066) and the Scientific Research Foundation (KYTZ201608) of CUIT and the major Project of Education Department in Sichuan (17ZA0063 and 2017JQ0030), and partially supported by the Sichuan international science and technology cooperation and exchange research program (2016HH0018).
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Luo, C., Li, X., Chen, Y., Wu, X., He, J., Zhou, J. (2019). CCNET: Cascading Convolutions for Cardiac Segmentation. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11633. Springer, Cham. https://doi.org/10.1007/978-3-030-24265-7_1
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DOI: https://doi.org/10.1007/978-3-030-24265-7_1
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