ArticleAJR. American journal of roentgenology2021
A 3D-2D Hybrid U-Net Convolutional Neural Network Approach to Prostate Organ Segmentation of Multiparametric MRI.
Article in AJR. American journal of roentgenology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it, 70 citations in OpenAlex.
- Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review.Radiology. Artificial intelligence · 2024Pooled it
- Radiomics-based approaches outperform visual analysis for differentiating lipoma from atypical lipomatous tumors: a review.Skeletal radiology · 2023Pooled it
- Discerning clinical target volume of endometrial cancer via a lightweight multi-phase delineation framework.Radiation oncology (London, England) · 2026Article
- 2D, 2.5D, or 3D? Comparing Dimensional Approaches in Deep Neural Networks for 3D Medical Image Analysis.Journal of imaging informatics in medicine · 2026Review
- From AI-based image analysis to surgical decision support in prostate cancer: interdisciplinary application of the international radiomics platform.Frontiers in oncology · 2026Article
- Biparametric MRI-based radiomics for noninvastive discrimination of benign prostatic hyperplasia nodules (BPH) and prostate cancer nodules: a bio-centric retrospective cohort study.Scientific reports · 2025Article
- SVNC-Net: An optimized U-Net variant with 2D convolutions for lightweight 3D spleen segmentation.PloS one · 2025Article
- DWI-based Biologically Interpretable Radiomic Nomogram for Predicting 1-year Biochemical Recurrence after Radical Prostatectomy: A Deep Learning, Multicenter Study.Current medical imaging · 2025Article
- Review of different convolutional neural networks used in segmentation of prostate during fusion biopsy.Central European journal of urology · 2025Review
- Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches.Bioengineering (Basel, Switzerland) · 2024Review
- Emerging and anticipated innovations in prostate cancer MRI and their impact on patient care.Abdominal radiology (New York) · 2024Review
- Segmentation of supragranular and infragranular layers in ultra-high-resolution 7T ex vivo MRI of the human cerebral cortex.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- A mixed Mamba U-net for prostate segmentation in MR images.Scientific reports · 2024Article
- Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management.Cancers · 2024Review
- Artificial Intelligence-Based Organ Delineation for Radiation Treatment Planning of Prostate Cancer on Computed Tomography.Advances in radiation oncology · 2024Article
- Inter-Rater Variability of Prostate Lesion Segmentation on Multiparametric Prostate MRI.Biomedicines · 2023Article
- Segmentation of supragranular and infragranular layers in ultra-high resolution 7TbioRxiv : the preprint server for biology · 2023Article
- Three-Dimensional Measurement of the Uterus on Magnetic Resonance Images: Development and Performance Analysis of an Automated Deep-Learning Tool.Diagnostics (Basel, Switzerland) · 2023Article
- Novel artificial intelligent transformer U-NET for better identification and management of prostate cancer.Molecular and cellular biochemistry · 2023Article
- Deep-Learning-Based Automatic Segmentation of Parotid Gland on Computed Tomography Images.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveProstate cancer is the most commonly diagnosed cancer in men in the United States with more than 200,000 new cases in 2018. Multiparametric MRI (mpMRI) is increasingly used for prostate cancer evaluation. Prostate organ segmentation is an essential step of surgical planning for prostate fusion biopsies. Deep learning convolutional neural networks (CNNs) are the predominant method of machine learning for medical image recognition. In this study, we describe a deep learning approach, a subset of artificial intelligence, for automatic localization and segmentation of prostates from mpMRI. MATERIALS AND
methodsThis retrospective study included patients who underwent prostate MRI and ultrasound-MRI fusion transrectal biopsy between September 2014 and December 2016. Axial T2-weighted images were manually segmented by two abdominal radiologists, which served as ground truth. These manually segmented images were used for training on a customized hybrid 3D-2D U-Net CNN architecture in a fivefold cross-validation paradigm for neural network training and validation. The Dice score, a measure of overlap between manually segmented and automatically derived segmentations, and Pearson linear correlation coefficient of prostate volume were used for statistical evaluation.
resultsThe CNN was trained on 299 MRI examinations (total number of MR images = 7774) of 287 patients. The customized hybrid 3D-2D U-Net had a mean Dice score of 0.898 (range, 0.890-0.908) and a Pearson correlation coefficient for prostate volume of 0.974.
conclusionA deep learning CNN can automatically segment the prostate organ from clinical MR images. Further studies should examine developing pattern recognition for lesion localization and quantification.
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Registered trials
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.