Region-growing fully convolutional neural network interactive segmentation of liver CT images

Chinese Journal of Liquid Crystals and Displays(2021)

引用 1|浏览6
Due to poor medical image quality, low contrast, large differences between patients, it is difficult for fully automatic segmentation methods to obtain sufficiently accurate and robust results. In order to solve the limitations of the automatic segmentation method, this paper proposes an improved region growing method based on neural network, and combined with the fully convolutional neural network to interactively segment liver CT images. Firstly, the image is preprocessed to highlight the liver area to be segmented. Then, the gradient value of the pixel under different edge detection operators is calculated as the feature of the pixel to form a pixel feature vector training network. The network takes a pair of pixel feature vectors as input and the correlation coefficient of two pixels as output. Then, the trained neural network model is used as the growth criterion of the region growing algorithm, and a point is manually selected interactively to generate the segmentation result. Finally, the segmentation result is connected as the interactive information of the original image and the gray channel of the original image and input into the full convolutional neural network. The experimental results show that the average Dice coefficient reaches 96. 69% , the pixel accuracy rate reaches 99. 62%, and the average intersection ratio reaches 96.65%. The results of liver segmentation in different abdominal CT image sequences show that this method can accurately extract liver regions and meet the needs of clinical applications.
fully convolutional neural network, region growing method, interactive segmentation
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