ArticleInternational journal of computer assisted radiology and surgery2018
Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging.
Article in International journal of computer assisted radiology and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 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
- Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review.Journal of medical systems · 2020Pooled it
- Automated Prostate Cancer Detection on T2-Weighted MRI Using a Dual-Stream Attention Network: A Study on Private Saudi Clinical Data and Public Benchmark Datasets.Journal of clinical medicine · 2026Article
- Prostate MR image segmentation using a multi-stage network approach.International urology and nephrology · 2026Article
- An investigation of race bias in deep learning-based segmentation of prostate MRI images.Scientific reports · 2025Article
- Clinical evaluation of the convolutional neural network‑based automatic delineation tool in determining the clinical target volume and organs at risk in rectal cancer radiotherapy.Oncology letters · 2024Article
- Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management.Cancers · 2024Review
- MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer.Journal of Zhejiang University. Science. B · 2023Review
- 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
- Integration of Deep Learning and Active Shape Models for More Accurate Prostate Segmentation in 3D MR Images.Journal of imaging · 2022Article
- Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation.European radiology · 2022Article
- Saliency-based 3D convolutional neural network for categorising common focal liver lesions on multisequence MRI.Insights into imaging · 2021Article
- 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
- Radiomics and Prostate MRI: Current Role and Future Applications.Journal of imaging · 2021Review
- Automatic contouring system for cervical cancer using convolutional neural networks.Medical physics · 2020Article
- Variability in accuracy of prostate cancer segmentation among radiologists, urologists, and scientists.Cancer medicine · 2020Article
- Segmentation of prostate and prostate zones using deep learning : A multi-MRI vendor analysis.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2020Article
- Automatic multi-organ segmentation in computed tomography images using hierarchical convolutional neural network.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Graph-convolutional-network-based interactive prostate segmentation in MR images.Medical physics · 2020Article
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Authors and funding
5 authors.
Funding
Abstract
purposeWe propose an approach of 3D convolutional neural network to segment the prostate in MR images.
methodsA 3D deep dense multi-path convolutional neural network that follows the framework of the encoder-decoder design is proposed. The encoder is built based upon densely connected layers that learn the high-level feature representation of the prostate. The decoder interprets the features and predicts the whole prostate volume by utilizing a residual layout and grouped convolution. A set of sub-volumes of MR images, centered at the prostate, is generated and fed into the proposed network for training purpose. The performance of the proposed network is compared to previously reported approaches.
resultsTwo independent datasets were employed to assess the proposed network. In quantitative evaluations, the proposed network achieved 95.11 and 89.01 Dice coefficients for the two datasets. The segmentation results were robust to variations in MR images. In comparison experiments, the segmentation performance of the proposed network was comparable to the previously reported approaches. In qualitative evaluations, the segmentation results by the proposed network were well matched to the ground truth provided by human experts.
conclusionsThe proposed network is capable of segmenting the prostate in an accurate and robust manner. This approach can be applied to other types of medical images.
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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.