ArticleRadiology. Imaging cancer2021
Segmentation of the Prostate Transition Zone and Peripheral Zone on MR Images with Deep Learning.
Article in Radiology. Imaging cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 57 citations in OpenAlex.
- Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review.Radiology. Artificial intelligence · 2024Pooled it
- Anatomy-aware fusion attention network for high-precision prostate MRI segmentation.Medical physics · 2026Article
- Integrating Artificial Intelligence into Prostate MR Imaging: Technical Foundations, Clinical Applications, and Workflow Implications.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Review
- Prostate MR image segmentation using a multi-stage network approach.International urology and nephrology · 2026Article
- Automatic segmentation of the urethra and prostate zones with deep learning on T2-weighted magnetic resonance imaging.Physics and imaging in radiation oncology · 2026Article
- Prostate cancer and benign prostatic hyperplasia lesions segmentation using diffusion kurtosis imaging, T2*, and R2* mapping with U-Net++ algorithm.Radiological physics and technology · 2026Article
- Non-Invasive Detection of Prostate Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative Radiological Interpretation: PROS-TD-AI.Journal of imaging · 2026Article
- From AI-based image analysis to surgical decision support in prostate cancer: interdisciplinary application of the international radiomics platform.Frontiers in oncology · 2026Article
- AI-driven precision diagnosis and treatment of prostate cancer: a narrative review.Frontiers in oncology · 2026Review
- A Neural Conditional Random Field Model Using Deep Features and Learnable Functions for End-to-End MRI Prostate Zonal Segmentation.The journal of machine learning for biomedical imaging · 2025Article
- Prognostic value of central gland volume on MRI for biochemical recurrence after prostate radiotherapy.Abdominal radiology (New York) · 2025Article
- Review of different convolutional neural networks used in segmentation of prostate during fusion biopsy.Central European journal of urology · 2025Review
- Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer.Abdominal radiology (New York) · 2024Article
- Comparison of data fusion strategies for automated prostate lesion detection using mpMRI correlated with whole mount histology.Radiation oncology (London, England) · 2024Article
- Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management.Cancers · 2024Review
- Deep learning performance on MRI prostate gland segmentation: evaluation of two commercially available algorithms compared with an expert radiologist.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Inter-Rater Variability of Prostate Lesion Segmentation on Multiparametric Prostate MRI.Biomedicines · 2023Article
- A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- The Evidence for Using Artificial Intelligence to Enhance Prostate Cancer MR Imaging.Current oncology reports · 2023Review
- CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI.IEEE transactions on medical imaging · 2023Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose To develop a deep learning model to delineate the transition zone (TZ) and peripheral zone (PZ) of the prostate on MR images. Materials and Methods This retrospective study was composed of patients who underwent a multiparametric prostate MRI and an MRI/transrectal US fusion biopsy between January 2013 and May 2016. A board-certified abdominal radiologist manually segmented the prostate, TZ, and PZ on the entire data set. Included accessions were split into 60% training, 20% validation, and 20% test data sets for model development. Three convolutional neural networks with a U-Net architecture were trained for automatic recognition of the prostate organ, TZ, and PZ. Model performance for segmentation was assessed using Dice scores and Pearson correlation coefficients. Results A total of 242 patients were included (242 MR images; 6292 total images). Models for prostate organ segmentation, TZ segmentation, and PZ segmentation were trained and validated. Using the test data set, for prostate organ segmentation, the mean Dice score was 0.940 (interquartile range, 0.930-0.961), and the Pearson correlation coefficient for volume was 0.981 (95% CI: 0.966, 0.989). For TZ segmentation, the mean Dice score was 0.910 (interquartile range, 0.894-0.938), and the Pearson correlation coefficient for volume was 0.992 (95% CI: 0.985, 0.995). For PZ segmentation, the mean Dice score was 0.774 (interquartile range, 0.727-0.832), and the Pearson correlation coefficient for volume was 0.927 (95% CI: 0.870, 0.957). Conclusion Deep learning with an architecture composed of three U-Nets can accurately segment the prostate, TZ, and PZ.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.