Bladder volume estimation based on USG images

Abstract

The article explores deep learning models in urological diagnostics to measure urinary bladder volume from medical images. It addresses the shortcomings of traditional methods by introducing advanced imaging techniques for more objective and precise analysis. The research employs Convolutional Neural Networks (CNNs) and the MONAI platform for image segmentation and analysis, using data from The Cancer Imaging Archive to focus on urological regions. Findings suggest these models enhance diagnostic accuracy but also highlight the need for further modifications to tailor them to specific medical data, underscoring machine learning’s significant role in accurate medical assessments for urology.

Keywords

• deep learning
• bladder volume estimation
• medical imaging convolutional neural networks
• image segmentation
• monai platform
• diagnostic accuracy