关键词: CT图像, 超像素, 卷积神经网络（CNN）, 模糊主动轮廓分割

Abstract:

This paper proposes a method for active contour segmentation of CT images based on super pixel and CNN. Firstly, the CT image is meshed and assigned a label by the super pixel SLIC method. Secondly, the meshed image is used as a dataset training CNN network to segment the super pixels of organs （such as liver, lung, etc.） and the super pixel’s seed point is connected to become the coarse segmentation boundary. Finally, the rough segmentation boundary is used as the initial contour, and the fuzzy active contour segmentation is performed to obtain the boundary of the organ in the CT image. After a large number of experimental comparisons, the segmental mean DSC coefficient of the lung CT image in this method reacheds 97%, and the average ASD coefficient reaches 1.23 mm. Compared with the reference algorithm in the liver CT image, the average VOE coefficient is reduced by 1% on the premise of ensuring the segmentation accuracy, and the segmentation time of the slice image is increased by 10 seconds on average.

Key words: CT image, super pixel, Convolutional Neural Network（CNN）, fuzzy active contour segmentation