Deep Learning and Unsupervised Fuzzy C-Means Based Level-Set Segmentation for Liver Tumor

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Deep Learning and Unsupervised Fuzzy C-Means Based Level-Set Segmentation for Liver Tumor


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Deep Learning and Unsupervised Fuzzy C-Means Based Level-Set Segmentation for Liver Tumor

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In this paper, we propose and validate a novel level-set method integrating an enhanced edge indicator and an automatically derived initial curve for CT based liver tumor segmentation. In the beginning, a 2D U-net is used to localize the liver and a 3D fully convolutional network (FCN) is used to refine the liver segmentation as well as to localize the tumor. The refined liver segmentation is used to remove non-liver tissues for subsequent tumor segmentation. Given that the tumor segmentation obtained from the aforementioned 3D FCN is typically imperfect, we adopt a novel level-set method to further improve the tumor segmentation. Specifically, the probabilistic distribution of the liver tumor is estimated using fuzzy c-means clustering and then utilized to enhance the object indication function used in level-set. The proposed segmentation pipeline was found to have an outstanding performance in terms of both liver and liver tumor.
In this paper, we propose and validate a novel level-set method integrating an enhanced edge indicator and an automatically derived initial curve for CT based liver tumor segmentation. In the beginning, a 2D U-net is used to localize the liver and a 3D fully convolutional network (FCN) is used to refine the liver segmentation as well as to localize the tumor. The refined liver segmentation is used to remove non-liver tissues for subsequent tumor segmentation. Given that the tumor segmentation obtained from the aforementioned 3D FCN is typically imperfect, we adopt a novel level-set method to further improve the tumor segmentation. Specifically, the probabilistic distribution of the liver tumor is estimated using fuzzy c-means clustering and then utilized to enhance the object indication function used in level-set. The proposed segmentation pipeline was found to have an outstanding performance in terms of both liver and liver tumor.