The Growing Role for Semantic Segmentation in Urology

• Published in: European urology focus Vol. 7; no. 4; pp. 692 - 695
• Main Authors: Rickman, Jack, Struyk, Griffin, Simpson, Benjamin, Byun, Benjamin C., Papanikolopoulos, Nikolaos
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.07.2021
• Subjects: Augmented reality; Computed tomography; Cross-sectional imaging; Deep Learning; Fuhrman grade; Gleason score; Humans; Image Processing, Computer-Assisted - methods; Machine learning; Magnetic resonance imaging; Radiomics; Reproducibility of Results; Semantic segmentation; Semantics; Simulation; Training; Urology; Gleason score; Training; Cross-sectional imaging; Semantic segmentation; Magnetic resonance imaging; Computed tomography; Simulation; Fuhrman grade; Machine learning; Augmented reality; Radiomics
• ISSN: 2405-4569; 2405-4569
• DOI: 10.1016/j.euf.2021.07.017

As the quantity and quality of cross-sectional imaging data increase, it is important to be able to make efficient use of the information. Semantic segmentation is an emerging technology that promises to improve the speed, reproducibility, and accuracy of analysis of medical imaging, and to allow visualization methods that were previously impossible. Manual image segmentation often requires expert knowledge and is both time- and cost-prohibitive in many clinical situations. However, automated methods, especially those using deep learning, show promise in alleviating this burden to make segmentation a standard tool for clinical intervention in the future. It is therefore important for clinicians to have a functional understanding of what segmentation is and to be aware of its uses. Here we include a number of examples of ways in which semantic segmentation has been put into practice in urology. This mini-review highlights the growing role of segmentation methods for medical images in urology to inform clinical practice. Segmentation methods show promise in improving the reliability of diagnosis and aiding in visualization, which may become a tool for patient education. Semantic segmentation is a promising technology for improving medical image analysis and powering computer-aided interventions. As the process becomes automated, it will become more prevalent in clinical practice.