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Recently, the need for liver transplantation has increased as the number of liver cancer and liver cirrhosis patients increases. The preoperative measurement of the liver volume of the donor is very important. The liver volume is one of analysis factors to predict liver function. However, the current process of liver volume is manually measured by radiologist from CT data, and it takes a lot of time and effort. In this paper, we propose a Deep 3D Attention U-Net for the whole liver segmentation that learns to focus on liver structures of varying shapes and sizes. In addition, the whole liver volume was calculated in voxel units using the segmentation result. The liver segmentation studies of the 266 patients are randomly assigned into train, validation and test sets, with a split ratio of 80%, 10% and 10% of total amount of patients, respectively. The results of liver segmentation achieved sensitivity of 0.914, the specificity of 0.999, and the dice similarity coefficient of 0.936. The relationship analysis of the liver volume showed the correlation coefficient r of 0.853 between manually measured liver volume and calculated liver volume using segmentation result. The results of liver volume measurements through whole liver segmentation based on Deep 3D Attention U-Net were similar to a reliable level.
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