The Growing Role for Semantic Segmentation in Urology

Jack Rickman, Griffin Struyk, Benjamin Simpson, Benjamin C. Byun, Nikolaos Papanikolopoulos

Research output: Contribution to journalReview articlepeer-review

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. PATIENT SUMMARY: 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.

Original languageEnglish (US)
Pages (from-to)692-695
Number of pages4
JournalEuropean Urology Focus
Volume7
Issue number4
DOIs
StatePublished - Jul 1 2021

Bibliographical note

Publisher Copyright:
Copyright © 2021 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Keywords

  • Augmented reality
  • Computed tomography
  • Cross-sectional imaging
  • Fuhrman grade
  • Gleason score
  • Machine learning
  • Magnetic resonance imaging
  • Radiomics
  • Semantic segmentation
  • Simulation
  • Training

PubMed: MeSH publication types

  • Journal Article
  • Review

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