ArticleMagma (New York, N.Y.)2021
Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition.
Article in Magma (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 29 citations in OpenAlex.
- A Narrative Review of Artificial Intelligence in MRI-Guided Prostate Cancer Diagnosis: Addressing Key Challenges.Diagnostics (Basel, Switzerland) · 2025Review
- Retrospectively Quantified T2 Improves Detection of Clinically Significant Peripheral Zone Prostate Cancer.Cancers · 2025Article
- Comparison of intensity normalization methods in prostate, brain, and breast cancer multi-parametric magnetic resonance imaging.Frontiers in oncology · 2025Article
- Integrated framework for quantitative T2-weighted MRI analysis following prostate cancer radiotherapy.Physics and imaging in radiation oncology · 2024Article
- The impact of pre-processing and disease characteristics on reproducibility of T2-weighted MRI radiomics features.Magma (New York, N.Y.) · 2023Article
- Magnetic resonance imaging preprocessing and radiomic features for classification of autosomal dominant polycystic kidney disease genotype.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- CROPro: a tool for automated cropping of prostate magnetic resonance images.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- A Framework of Analysis to Facilitate the Harmonization of Multicenter Radiomic Features in Prostate Cancer.Journal of clinical medicine · 2022Article
- Pseudo-T2 mapping for normalization of T2-weighted prostate MRI.Magma (New York, N.Y.) · 2022Article
- Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges.European radiology experimental · 2022Review
- A New Framework for Precise Identification of Prostatic Adenocarcinoma.Sensors (Basel, Switzerland) · 2022Article
- Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives.Diagnostics (Basel, Switzerland) · 2021Review
- Utility of TScientific reports · 2021Article
- A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI.Diagnostics (Basel, Switzerland) · 2020Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
objectivesTo develop and evaluate an automated method for prostate T2-weighted (T2W) image normalization using dual-reference (fat and muscle) tissue. MATERIALS AND
methodsTransverse T2W images from the publicly available PROMISE12 (N = 80) and PROSTATEx (N = 202) challenge datasets, and an in-house collected dataset (N = 60) were used. Aggregate channel features object detectors were trained to detect reference fat and muscle tissue regions, which were processed and utilized to normalize the 3D images by linear scaling. Mean prostate pseudo T2 values after normalization were compared to literature values. Inter-patient histogram intersections of voxel intensities in the prostate were compared between our approach, the original images, and other commonly used normalization methods. Healthy vs. malignant tissue classification performance was compared before and after normalization.
resultsThe prostate pseudo T2 values of the three tested datasets (mean ± standard deviation = 78.49 ± 9.42, 79.69 ± 6.34 and 79.29 ± 6.30 ms) corresponded well to T2 values from literature (80 ± 34 ms). Our normalization approach resulted in significantly higher (p < 0.001) inter-patient histogram intersections (median = 0.746) than the original images (median = 0.417) and most other normalization methods. Healthy vs. malignant classification also improved significantly (p < 0.001) in peripheral (AUC 0.826 vs. 0.769) and transition (AUC 0.743 vs. 0.678) zones.
conclusionAn automated dual-reference tissue normalization of T2W images could help improve the quantitative assessment of prostate cancer.
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