ArticleJournal of medical imaging (Bellingham, Wash.)2017
Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted MRI using convolutional neural networks.
Article in Journal of medical imaging (Bellingham, Wash.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review.Radiology. Artificial intelligence · 2024Pooled it
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Next-Generation Advances in Prostate Cancer Imaging and Artificial Intelligence Applications.Journal of imaging · 2025Review
- Toward a Refined PI-RADS: The Feasibility and Limitations of More Informative Metrics in Reviewing MRI Scans.Journal of magnetic resonance imaging : JMRI · 2025Review
- A Pilot Study of PSMA PET/CT and MRI Fusion for Prostate Cancer: Software to Replace PET/MRI Hardware.Journal of clinical medicine · 2024Article
- Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer.Abdominal radiology (New York) · 2024Article
- Noninvasive Prediction of Sperm Retrieval Using Diffusion Tensor Imaging in Patients with Nonobstructive Azoospermia.Journal of imaging · 2023Article
- Modality preserving U-Net for segmentation of multimodal medical images.Quantitative imaging in medicine and surgery · 2023Article
- Automatic segmentation of prostate zonal anatomy on MRI: a systematic review of the literature.Insights into imaging · 2022Article
- Explainable AI for CNN-based prostate tumor segmentation in multi-parametric MRI correlated to whole mount histopathology.Radiation oncology (London, England) · 2022Article
- Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities.Diagnostics (Basel, Switzerland) · 2022Review
- Automatic PI-RADS assignment by means of formal methods.La Radiologia medica · 2022Article
- Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges.Diagnostics (Basel, Switzerland) · 2021Review
- Deep learning based segmentation of brain tissue from diffusion MRI.NeuroImage · 2021Article
- Segmentation of the Prostate Transition Zone and Peripheral Zone on MR Images with Deep Learning.Radiology. Imaging cancer · 2021Article
- Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies.Frontiers in oncology · 2021Review
- Harnessing clinical annotations to improve deep learning performance in prostate segmentation.PloS one · 2021Article
- Role of standard and soft tissue chest radiography images in deep-learning-based early diagnosis of COVID-19.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Deep Learning in Prostate Cancer Diagnosis Using Multiparametric Magnetic Resonance Imaging With Whole-Mount Histopathology Referenced Delineations.Frontiers in medicine · 2021Article
- Data Augmentation and Transfer Learning to Improve Generalizability of an Automated Prostate Segmentation Model.AJR. American journal of roentgenology · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer is a leading cause of cancer-related death among men. Multiparametric magnetic resonance imaging has become an essential part of the diagnostic evaluation of prostate cancer. The internationally accepted interpretation scheme (Pi-Rads v2) has different algorithms for scoring of the transition zone (TZ) and peripheral zone (PZ) of the prostate as tumors can appear different in these zones. Computer-aided detection tools have shown different performances in TZ and PZ and separating these zones for training and detection is essential. The TZ-PZ segmentation which requires the segmentation of prostate whole gland and TZ is typically done manually. We present a fully automatic algorithm for delineation of the prostate gland and TZ in diffusion-weighted imaging (DWI) via a stack of fully convolutional neural networks. The proposed algorithm first detects the slices that contain a portion of prostate gland within the three-dimensional DWI volume and then it segments the prostate gland and TZ automatically. The segmentation stage of the algorithm was applied to DWI images of 104 patients and median Dice similarity coefficients of 0.93 and 0.88 were achieved for the prostate gland and TZ, respectively. The detection of image slices with and without prostate gland had an average accuracy of 0.97.
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