The Growing Role for Semantic Segmentation in Urology [journal]

Journal

European Urology Focus - August 18, 2021

Authors

Jack Rickman (undergraduate student researcher), Griffin Struyk, Benjamin Simpson, Benjamin C Byun, Nikolaos Papanikolopoulos (professor)

Abstract

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.

Link to full paper

The Growing Role for Semantic Segmentation in Urology

Keywords

machine learning, augmented reality