ArticleInsights into imaging2021
Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology.
Article in Insights into imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
- Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review.Radiology. Artificial intelligence · 2024Pooled it
- Utility of PSA density velocity in diagnosing prostate cancer on prostate MR.Abdominal radiology (New York) · 2026Article
- Importance of the Quality of Annotation: Impact of Simulated Inter-Observer Variability on Deep Neural Network Automated Segmentation Model Performance.Bioengineering (Basel, Switzerland) · 2026Article
- Assessing inter-observer variability in prostate and GTV segmentation on mpMRI: A comparison between radiation oncologists and AI-based method.Journal of applied clinical medical physics · 2026Article
- Comparison between artificial intelligence-based and manual organ delineations in pretreatment computed tomography scans of prostate cancer patients: a visual grading study.Radiation protection dosimetry · 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
- A clinically informed automated evaluation pipeline for medical image segmentation based on Medical Similarity Index.Physics and imaging in radiation oncology · 2026Article
- Challenges in Using Deep Neural Networks Across Multiple Readers in Delineating Prostate Gland Anatomy.Journal of imaging informatics in medicine · 2026Article
- Bridging engineering and neuro-oncology: a scalable FastAPI-deployed CNN framework for real-time explainable brain tumor diagnosis.Frontiers in neuroscience · 2026Article
- Accuracy, precision, and efficiency of manual planimetry versus point counting for sectional area estimation in oral morphometry: a comparison using a simulated two-dimensional reference model.Frontiers in oral health · 2026Article
- An investigation of race bias in deep learning-based segmentation of prostate MRI images.Scientific reports · 2025Article
- Evaluating the quality of multiple automatically produced segmentation variants of the prostate on Magnetic Resonance Imaging scans for brachytherapy.Physics and imaging in radiation oncology · 2025Article
- Comparing AI and Manual Segmentation of Prostate MRI: Towards AI-Driven 3D-Model-Guided Prostatectomy.Diagnostics (Basel, Switzerland) · 2025Article
- Impact of uncertainty quantification through conformal prediction on volume assessment from deep learning-based MRI prostate segmentation.Insights into imaging · 2024Article
- Automated prostate gland segmentation in challenging clinical cases: comparison of three artificial intelligence methods.Abdominal radiology (New York) · 2024Article
- Automatic AI-based contouring of prostate MRI for online adaptive radiotherapy.Zeitschrift fur medizinische Physik · 2024Article
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
- Prostate volume analysis in image registration for prostate cancer care: a verification study.Physical and engineering sciences in medicine · 2023Article
- Interobserver Agreement in Automatic Segmentation Annotation of Prostate Magnetic Resonance Imaging.Bioengineering (Basel, Switzerland) · 2023Article
- Development trends and knowledge framework in the application of magnetic resonance imaging in prostate cancer: a bibliometric analysis from 1984 to 2022.Quantitative imaging in medicine and surgery · 2023Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate prostate zonal segmentation on magnetic resonance images (MRI) is a critical prerequisite for automated prostate cancer detection. We aimed to assess the variability of manual prostate zonal segmentation by radiologists on T2-weighted (T2W) images, and to study factors that may influence it.
methodsSeven radiologists of varying levels of experience segmented the whole prostate gland (WG) and the transition zone (TZ) on 40 axial T2W prostate MRI images (3D T2W images for all patients, and both 3D and 2D images for a subgroup of 12 patients). Segmentation variabilities were evaluated based on: anatomical and morphological variation of the prostate (volume, retro-urethral lobe, intensity contrast between zones, presence of a PI-RADS ≥ 3 lesion), variation in image acquisition (3D vs 2D T2W images), and reader's experience. Several metrics including Dice Score (DSC) and Hausdorff Distance were used to evaluate differences, with both a pairwise and a consensus (STAPLE reference) comparison.
resultsDSC was 0.92 (± 0.02) and 0.94 (± 0.03) for WG, 0.88 (± 0.05) and 0.91 (± 0.05) for TZ respectively with pairwise comparison and consensus reference. Variability was significantly (p < 0.05) lower for the mid-gland (DSC 0.95 (± 0.02)), higher for the apex (0.90 (± 0.06)) and the base (0.87 (± 0.06)), and higher for smaller prostates (p < 0.001) and when contrast between zones was low (p < 0.05). Impact of the other studied factors was non-significant.
conclusionsVariability is higher in the extreme parts of the gland, is influenced by changes in prostate morphology (volume, zone intensity ratio), and is relatively unaffected by the radiologist's level of expertise.
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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.