ArticleEuropean radiology2024
Manual prostate MRI segmentation by readers with different experience: a study of the learning progress.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Importance of the Quality of Annotation: Impact of Simulated Inter-Observer Variability on Deep Neural Network Automated Segmentation Model Performance.Bioengineering (Basel, Switzerland) · 2026Article
- AI-based prostate volume estimation from multi-planar MRI under variable acquisition protocols.European journal of radiology open · 2026Article
- Evaluation of AI for prostate cancer detection in biparametric-MRI screening population data.European radiology · 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
- PrecisionPro Fusion: Clinical validation of an automated, rigid, Prostate-Specific MRI-CT Fusion system for prostate radiotherapy planning.Technical innovations & patient support in radiation oncology · 2025Article
- Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Liver margin segmentation in abdominal CT images using U-Net and Detectron2: annotated dataset for deep learning models.Scientific reports · 2025Article
- Impact of uncertainty quantification through conformal prediction on volume assessment from deep learning-based MRI prostate segmentation.Insights into imaging · 2024Article
- MRI radiomics based on deep learning automated segmentation to predict early recurrence of hepatocellular carcinoma.Insights into imaging · 2024Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
objectiveTo evaluate the learning progress of less experienced readers in prostate MRI segmentation. MATERIALS AND
methodsOne hundred bi-parametric prostate MRI scans were retrospectively selected from the Göteborg Prostate Cancer Screening 2 Trial (single center). Nine readers with varying degrees of segmentation experience were involved: one expert radiologist, two experienced radiology residents, two inexperienced radiology residents, and four novices. The task was to segment the whole prostate gland. The expert's segmentations were used as reference. For all other readers except three novices, the 100 MRI scans were divided into five rounds (cases 1-10, 11-25, 26-50, 51-76, 76-100). Three novices segmented only 50 cases (three rounds). After each round, a one-on-one feedback session between the expert and the reader was held, with feedback on systematic errors and potential improvements for the next round. Dice similarity coefficient (DSC) > 0.8 was considered accurate.
resultsUsing DSC > 0.8 as the threshold, the novices had a total of 194 accurate segmentations out of 250 (77.6%). The residents had a total of 397/400 (99.2%) accurate segmentations. In round 1, the novices had 19/40 (47.5%) accurate segmentations, in round 2 41/60 (68.3%), and in round 3 84/100 (84.0%) indicating learning progress.
conclusionsRadiology residents, regardless of prior experience, showed high segmentation accuracy. Novices showed larger interindividual variation and lower segmentation accuracy than radiology residents. To prepare datasets for artificial intelligence (AI) development, employing radiology residents seems safe and provides a good balance between cost-effectiveness and segmentation accuracy. Employing novices should only be considered on an individual basis. CLINICAL RELEVANCE STATEMENT: Employing radiology residents for prostate MRI segmentation seems safe and can potentially reduce the workload of expert radiologists. Employing novices should only be considered on an individual basis. KEY POINTS: • Using less experienced readers for prostate MRI segmentation is cost-effective but may reduce quality. • Radiology residents provided high accuracy segmentations while novices showed large inter-reader variability. • To prepare datasets for AI development, employing radiology residents seems safe and might provide a good balance between cost-effectiveness and segmentation accuracy while novices should only be employed on an individual basis.
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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.