The segmentation of bladder cancer using the voxel-features-based method

Hao-jie Zheng; Xiao-pan Xu; Xi Zhang; Hong-bing Lu; Ji-min Liang; Yang Liu

doi:10.1117/12.2512082

15 March 2019 The segmentation of bladder cancer using the voxel-features-based method

Hao-jie Zheng, Xiao-pan Xu, Xi Zhang, Hong-bing Lu, Ji-min Liang, Yang Liu

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 109492F (2019) https://doi.org/10.1117/12.2512082
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Accurate segmentation of bladder cancer is the basis for determining the staging of bladder cancer. In our previous study, we have segmented the inner and outer surface of bladder wall and obtained the candidate region of bladder cancer, however, it is hard to segment the cancer region from the candidate region. To segment the cancer region accurately, we proposed a voxel-feature-based method and extracted 1159 features from each voxel of candidate region. After feature extraction, the recursive feature elimination-based support vector machine classifier (SVM-RFE) method was adopted to obtain an optimal feature subset for the classification of the cancer and the wall regions. According to feature selection and ranking, 125 top-ranked features were selected as the optimal subset, with an area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of 1, 99.99%, 99.98%, and 1. Using the optimal subset, we calculated the probability value of each voxel belonging to the cancer region, then obtained the boundary to separate the tumor and wall regions. The mean DSC of the segmentation results in the testing set is 0.9127, indicating that the proposed method can accurately segment the bladder cancer region.

Citation Download Citation

Hao-jie Zheng, Xiao-pan Xu, Xi Zhang, Hong-bing Lu, Ji-min Liang, and Yang Liu "The segmentation of bladder cancer using the voxel-features-based method", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109492F (15 March 2019); https://doi.org/10.1117/12.2512082

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