Automatic liver segmentation with CT images based on 3D U-net deep learning approach

Ting-Yu Su; Wei-Tse Yang; Tsu-Chi Cheng; Yi-Fei He; Yu-Hua Fang

doi:10.1117/12.2521640

27 March 2019 Automatic liver segmentation with CT images based on 3D U-net deep learning approach

Ting-Yu Su, Wei-Tse Yang, Tsu-Chi Cheng, Yi-Fei He, Yu-Hua Fang

Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 110500V (2019) https://doi.org/10.1117/12.2521640
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

Abstract

The detection and the evaluation of the shape of liver from abdominal computed tomography (CT) images are fundamental tasks in the computer-assisted liver surgery planning such as radiation therapy. However, the segmentation of the liver still remains many challenges to be solved, such as ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we developed an automatic liver segmentation model based on 3D U-net network. Some preprocessing steps were done to elevate the performance of our protocol first. Also, an approximate liver map was generated by calculating the gradient of CT images. The area which had high possibility to be liver was select as the training set to make sure the balance of data. Then, a deep learning U-net structure was applied for the processed training data. Finally, some post-processing methods, which include k-means clustering and morphology algorithms, was applied in our protocol. Our protocol showed the results with high structure similarity index (SSIM), dice score coefficient and peak signal-to noise ratio (PSNR) of liver segmentation model, demonstrating the potential clinical applicability of the proposed approach.

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Ting-Yu Su, Wei-Tse Yang, Tsu-Chi Cheng, Yi-Fei He, and Yu-Hua Fang "Automatic liver segmentation with CT images based on 3D U-net deep learning approach", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500V (27 March 2019); https://doi.org/10.1117/12.2521640

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KEYWORDS

Liver

3D modeling

Image segmentation

Spleen

Computed tomography

Data modeling

Convolution

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