Automatic urinary bladder detection from medical computed tomography scans using convolutional neural network

Abstract

This paper introduces a system for detecting and evaluating an algorithm that segments the urinary bladder in medical images obtained from contrast-less computed tomography (CT) scans of patients with bladder tumors. Multiple segmentation methods are needed in situations where tumors in the bladder cause structural changes that appear as irregularities in images, complicating the slicing process. The segmentation process begins with viewing the urinary bladder DICOM in three different perspectives, and then enhancing the image to expand the dataset. Next, the areas of the urinary bladder are pinpointed, with the urinary bladder dataset being split into 70% for training and 30% for testing to distinguish it from the nearby tissues, organs, and bones. The suggested system was evaluated on eight 3D CT images obtained from the cancer imaging archive (TCIA). Results from the experiment show that the designed system is effective in identifying and delineating the urinary bladder.