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Abstract #4847

A Two-Step Automated Liver MR Images Quality Assessment based on Convolutional Neural Network

Yida Wang1, Yang Song1, Fang Wang2, Zhe Han2, Lei Shi2, Guoliang Shao2, and Guang Yang1

1Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China, 2Zhejiang Cancer Hospital, Zhejiang, China

We proposed a two-step approach to evaluate automatically liver MR image quality. Firstly, we used a U-Net to segment the liver region. Then image patches were extracted from this region and another CNN was applied to estimate the quality of each image patch. The quality of the entire image was calculated based on the total percentage of 'bad' image patches in all patches. Receiver operating characteristic curve and confusion matrix were used to evaluate the performance of the proposed method. The performance of our method was comparable to human image readers.

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