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KMID : 1149020220240020055
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Journal of Korean Society of Computed Tomographic Technology
2022 Volume.24 No. 2 p.55 ~ p.64
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Model Complexity of Deep Residual U-NET for CT Liver Volumetry
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Park Koung-Jin
Park Sang-Hyub
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Abstract
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Computed Tomography (CT) has been used for liver volume measurement because of the highest location accuracy. Automated segmentation methods may improve CT volumetry time, but it has low accuracy. Residual U-Net which is one of the deep learning methods could improve segmentation accuracy. However optimization of residual U-Net hasn’t been demonstrated yet. The purpose of this paper is to investigate the optimal complexity for CT liver volumetry. The study was conducted using the 3D-IRCADb01 Datasets (10 males, 10 females) published by MIS Training Center, 15 people learned and 5 people tested. Segmented images were generated using Deep Residual U-Nets with a total of four different complexity. As a result, as the model became more complex, the total parameters and training time increased exponentially. In all models, both training and testing showed more than 97% accuracy. All losses were less than 0.2. In the case of DCL, it was the lowest at 0.8037 in 3-layer and the highest at 0.9533 in 5-layer. In conclusion, 5 hidden layers of residual U-Net has the highest dice coefficient loss and could train the datasets faster than other complex models.
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KEYWORD
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CT Volumetry, Liver, Deep Residual U-Net, Deep learning
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