期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2018
卷号：9
期号：4
DOI：10.14569/IJACSA.2018.090464
出版社：Science and Information Society (SAI)
摘要：Segmentation of the whole-cardiac CT image sequence is the key to computer-aided diagnosis and study of lesions in the heart. Due to the dilation, contraction and the flow of the blood, the cardiac CT images are prone to weak boundaries and artifacts. Traditional manual segmentation methods are time-consuming and labor-intensive to produce over-segmentation. Therefore, an automatic cardiac CT image sequence segmentation technique is proposed. This technique was employed using deep learning algorithm to understand the segmentation function from the ground truth data. Using the convolution neural network (CNN) on the central location of the heart, filtering ribs, muscles and other contrasting contrast are not an obvious part of the removal of the heart area. Staked denoising auto-encoders are used to automatically deduce the contours of the heart. Therefore, nine cardiac CT image sequence datasets are used to validate the method. The results showed that the algorithm proposed in this paper has best segmentation impact to such cardiac CT images which have a complex background, the distinctness between the background and the target area which is not obvious; and the internal structure diversification. It can filter out most of the non-heart tissue part, which is more conducive to the doctor observing patient’s heart health.
关键词：Cardiac CT; segmentation; deep learning; automatic location; contour inference
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