ArticleAdvances in radiation oncology
Segmentation of the Prostatic Gland and the Intraprostatic Lesions on Multiparametic Magnetic Resonance Imaging Using Mask Region-Based Convolutional Neural Networks.
Article in Advances in radiation oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 48 citations in OpenAlex.
- Artificial Intelligence in Prostate MRI: Addressing Current Limitations Through Emerging Technologies.Journal of magnetic resonance imaging : JMRI · 2026Review
- PARALLEL ROUTE COHERENT MIXUP DEEP LEARNING FOR SINGLE SOURCE TO MULTI-DOMAIN GENERALIZED PROSTATE CANCER SEGMENTATION FOR MR GUIDED ADAPTIVE RADIOTHERAPY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026Article
- Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector.Scientific reports · 2025Article
- Geometric and dosimetric evaluation for breast and regional nodal auto-segmentation structures.Journal of applied clinical medical physics · 2024Article
- Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI.Radiology · 2024Article
- Deep learning-based dominant index lesion segmentation for MR-guided radiation therapy of prostate cancer.Medical physics · 2023Article
- Up-to-Date Imaging and Diagnostic Techniques for Prostate Cancer: A Literature Review.Diagnostics (Basel, Switzerland) · 2023Review
- Automated prostate multi-regional segmentation in magnetic resonance using fully convolutional neural networks.European radiology · 2023Article
- Deep learning-based segmentation of prostatic urethra on computed tomography scans for treatment planning.Physics and imaging in radiation oncology · 2023Article
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- Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives.Frontiers in oncology · 2023Review
- MRI-based prostate and dominant lesion segmentation using cascaded scoring convolutional neural network.Medical physics · 2022Article
- Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI.Magma (New York, N.Y.) · 2022Review
- Explainable AI for CNN-based prostate tumor segmentation in multi-parametric MRI correlated to whole mount histopathology.Radiation oncology (London, England) · 2022Article
- Article
- Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges.Diagnostics (Basel, Switzerland) · 2021Review
- Domain adaptation for segmentation of critical structures for prostate cancer therapy.Scientific reports · 2021Article
- Automatic segmentation of the carotid artery and internal jugular vein from 2D ultrasound images for 3D vascular reconstruction.International journal of computer assisted radiology and surgery · 2020Article
- Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends.Cancers · 2020Review
- The Top 10 Downloads of 2020 in ASTRO'sAdvances in radiation oncologyArticle
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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
purposeAccurate delineation of the prostate gland and intraprostatic lesions (ILs) is essential for prostate cancer dose-escalated radiation therapy. The aim of this study was to develop a sophisticated deep neural network approach to magnetic resonance image analysis that will help IL detection and delineation for clinicians. METHODS AND MATERIALS: We trained and evaluated mask region-based convolutional neural networks to perform the prostate gland and IL segmentation. There were 2 cohorts in this study: 78 public patients (cohort 1) and 42 private patients from our institution (cohort 2). Prostate gland segmentation was performed using T2-weighted images (T2WIs), although IL segmentation was performed using T2WIs and coregistered apparent diffusion coefficient maps with prostate patches cropped out. The IL segmentation model was extended to select 5 highly suspicious volumetric lesions within the entire prostate.
resultsThe mask region-based convolutional neural networks model was able to segment the prostate with dice similarity coefficient (DSC) of 0.88 ± 0.04, 0.86 ± 0.04, and 0.82 ± 0.05; sensitivity (Sens.) of 0.93, 0.95, and 0.95; and specificity (Spec.) of 0.98, 0.85, and 0.90. However, ILs were segmented with DSC of 0.62 ± 0.17, 0.59 ± 0.14, and 0.38 ± 0.19; Sens. of 0.55 ± 0.30, 0.63 ± 0.28, and 0.22 ± 0.24; and Spec. of 0.974 ± 0.010, 0.964 ± 0.015, and 0.972 ± 0.015 in public validation/public testing/private testing patients when trained with patients from cohort 1 only. When trained with patients from both cohorts, the values were as follows: DSC of 0.64 ± 0.11, 0.56 ± 0.15, and 0.46 ± 0.15; Sens. of 0.57 ± 0.23, 0.50 ± 0.28, and 0.33 ± 0.17; and Spec. of 0.980 ± 0.009, 0.969 ± 0.016, and 0.977 ± 0.013.
conclusionsOur research framework is able to perform as an end-to-end system that automatically segmented the prostate gland and identified and delineated highly suspicious ILs within the entire prostate. Therefore, this system demonstrated the potential for assisting the clinicians in tumor delineation.
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Registered trials
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