Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review-Reference-Cited by-同舟云学术

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Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review

Published:2024-07-01 Issue:4 Volume:6 Page:
ISSN:2638-6100
Container-title:Radiology: Artificial Intelligence
language:en
Short-container-title:Radiology: Artificial Intelligence

Author:

Fassia Mohammad-Kasim¹^ORCID,Balasubramanian Adithya¹^ORCID,Woo Sungmin¹^ORCID,Vargas Hebert Alberto¹^ORCID,Hricak Hedvig¹^ORCID,Konukoglu Ender¹^ORCID,Becker Anton S.¹^ORCID

Affiliation:

1. From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.).

Publisher

Radiological Society of North America (RSNA)

Link

http://pubs.rsna.org/doi/pdf/10.1148/ryai.230138

Reference63 articles.

1. Early Detection of Prostate Cancer: AUA Guideline

2. Comparison of PI-RADS Versions 2.0 and 2.1 for MRI-based Calculation of the Prostate Volume

3. Repeatability of diffusion-weighted MRI of the prostate using whole lesion ADC values, skew and histogram analysis

4. Manual prostate cancer segmentation in MRI: interreader agreement and volumetric correlation with transperineal template core needle biopsy

5. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement

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