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.
Bibliographical noteFunding Information:
Acknowledgments: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA225435 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
© 2021 European Association of Urology
- Augmented reality
- Computed tomography
- Cross-sectional imaging
- Fuhrman grade
- Gleason score
- Machine learning
- Magnetic resonance imaging
- Semantic segmentation
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural