Deep learning prostate MRI segmentation accuracy and robustness: A systematic review Review
| Authors: | Fassia, M. K.; Balasubramanian, A.; Woo, S.; Vargas, H. A.; Hricak, H.; Konukoglu, E.; Becker, A. S. |
| Review Title: | Deep learning prostate MRI segmentation accuracy and robustness: A systematic review |
| Abstract: | Purpose: To investigate the accuracy and robustness of prostate segmentation using deep learning across various training data sizes, MRI vendors, prostate zones, and testing methods relative to fellowship-trained diagnostic radiologists. Materials and Methods: In this systematic review, Embase, PubMed, Scopus, and Web of Science databases were queried for English-language articles using keywords and related terms for prostate MRI segmentation and deep learning algorithms dated to July 31, 2022. A total of 691 articles from the search query were collected and subsequently filtered to 48 on the basis of predefined inclusion and exclusion criteria. Multiple characteristics were extracted from selected studies, such as deep learning algorithm performance, MRI vendor, and training dataset features. The primary outcome was comparison of mean Dice similarity coefficient (DSC) for prostate segmentation for deep learning algorithms versus diagnostic radiologists. Results: Forty-eight studies were included. Most published deep learning algorithms for whole prostate gland segmentation (39 of 42 [93%]) had a DSC at or above expert level (DSC ≥ 0.86). The mean DSC was 0.79 ± 0.06 (SD) for peripheral zone, 0.87 ± 0.05 for transition zone, and 0.90 ± 0.04 for whole prostate gland segmentation. For selected studies that used one major MRI vendor, the mean DSCs of each were as follows: General Electric (three of 48 studies), 0.92 ± 0.03; Philips (four of 48 studies), 0.92 ± 0.02; and Siemens (six of 48 studies), 0.91 ± 0.03. Conclusion: Deep learning algorithms for prostate MRI segmentation demonstrated accuracy similar to that of expert radiologists despite varying parameters; therefore, future research should shift toward evaluating segmentation robustness and patient outcomes across diverse clinical settings. © 2024, Radiological Society of North America Inc.. All rights reserved. |
| Keywords: | nuclear magnetic resonance imaging; prostate; correlation coefficient; systematic review; artificial intelligence; mri; dice similarity coefficient; support vector machine; prostate segmentation; human; article; deep learning; convolutional neural network; segmentation algorithm; diagnostic radiologist; genital/reproductive |
| Journal Title: | Radiology: Artificial Intelligence |
| Volume: | 6 |
| Issue: | 4 |
| ISSN: | 2638-6100 |
| Publisher: | Radiological Society of North America, Inc. |
| Date Published: | 2024-07-01 |
| Start Page: | e230138 |
| Language: | English |
| DOI: | 10.1148/ryai.230138 |
| PROVIDER: | scopus |
| PMCID: | PMC11294957 |
| PUBMED: | 38568094 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF -- Source: Scopus |
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