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Objectives: Regional bladder wall thickening on noninvasive magnetic resonance (MR) images is an important sign of developing urinary bladder cancer (BCa), and precise segmentation of the tumor mass is an essential step toward noninvasive identification of the pathological stage and grade, which is of critical importance for the clinical management of patients with BCa. Methods: In this paper, we proposed a new method based on the high-throughput pixel-level features and a random forest (RF) classifier for the BCa segmentation. First, regions of interest (ROIs) including tumor and wall ROIs were used in the training set for feature extraction and segmentation model development. Then, candidate regions containing both bladder tumor and its neighboring wall tissue in the testing set were segmented. Results: Experimental results were evaluated on a retrospective database containing 56 patients postoperatively confirmed with BCa from the affiliated hospital. The Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) of the tumor regions were adopted to quantitatively assess the overall performance of this approach. The results showed that the mean DSC was 0.906 (95% confidential interval [CI]: 0.852-0.959), and the mean ASSD was 1.190 mm (95% CI: 1.727-2.449), which were higher than those of the state-of-the-art methods for tumor region separation. Conclusion: The proposed Pixel-level BCa segmentation method can achieve good performance for the accurate segmentation of BCa lesion on MR images.

PIxel-Level Segmentation of Bladder Tumors on MR Images Using a Random Forest Classifier