A liver tumor segmentation method based on cascaded fully convolutional neural networks and 3D conditional random fields

Feng Wang; Jinquan Hou; Shanshan Wu

doi:10.1117/12.2646802

7 September 2022 A liver tumor segmentation method based on cascaded fully convolutional neural networks and 3D conditional random fields

Feng Wang, Jinquan Hou, Shanshan Wu

Author Affiliations +

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123290N (2022) https://doi.org/10.1117/12.2646802
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

Aiming at the following problems encountered by Multiple Supervised Fully Convolutional Networks (MSFCN ) in liver tumor segmentation: rich blood vessels around the liver, complex organs, and inconspicuous boundaries, which have a great impact on accurate tumor segmentation, plus The tumor boundary is not clear and the segmentation is difficult. In this paper, we propose a method to automatically segment liver and tumor in CT abdominal images using Cascaded Fully Convolutional Networks (CFCN) and 3D Conditional Random Fields (CRF). We train and cascade two fully convolutional neural networks for combined segmentation of the liver and its tumors. In the first step, our trained FCN segmented the liver from abdominal CT images as the input of the second FCN, which removed the influence of the environment around the liver. The second FCN segmented the tumor region only based on the liver obtained in the first step. Finally, 3D conditional random field is used to optimize the segmented image obtained by cascaded FCN, and the edge information of liver tumor image is extracted to solve the problem of unclear boundary of liver tumor and complete the segmentation.

Citation Download Citation

Feng Wang, Jinquan Hou, and Shanshan Wu "A liver tumor segmentation method based on cascaded fully convolutional neural networks and 3D conditional random fields", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123290N (7 September 2022); https://doi.org/10.1117/12.2646802

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