ArticleJournal of digital imaging2017
Accuracy Validation of an Automated Method for Prostate Segmentation in Magnetic Resonance Imaging.
Article in Journal of digital imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Manual prostate MRI segmentation by readers with different experience: a study of the learning progress.European radiology · 2024Article
- DMCA-GAN: Dual Multilevel Constrained Attention GAN for MRI-Based Hippocampus Segmentation.Journal of digital imaging · 2023Article
- Semantic Segmentation of Pancreatic Cancer in Endoscopic Ultrasound Images Using Deep Learning Approach.Cancers · 2022Article
- 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
- Automatic zonal segmentation of the prostate from 2D and 3D T2-weighted MRI and evaluation for clinical use.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Article
- Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology.Insights into imaging · 2021Article
- Methodological approach to create an atlas using a commercial auto-contouring software.Journal of applied clinical medical physics · 2020Article
- Evaluation of Multimodal Algorithms for the Segmentation of Multiparametric MRI Prostate Images.Computational and mathematical methods in medicine · 2020Article
- A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling.Proceedings of SPIE--the International Society for Optical Engineering · 2019Article
- Prostate zonal segmentation in 1.5T and 3T T2W MRI using a convolutional neural network.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Accuracy of tumor segmentation from multi-parametric prostate MRI andEJNMMI research · 2018Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 2 countries.
Funding
Abstract
Three dimensional (3D) manual segmentation of the prostate on magnetic resonance imaging (MRI) is a laborious and time-consuming task that is subject to inter-observer variability. In this study, we developed a fully automatic segmentation algorithm for T2-weighted endorectal prostate MRI and evaluated its accuracy within different regions of interest using a set of complementary error metrics. Our dataset contained 42 T2-weighted endorectal MRI from prostate cancer patients. The prostate was manually segmented by one observer on all of the images and by two other observers on a subset of 10 images. The algorithm first coarsely localizes the prostate in the image using a template matching technique. Then, it defines the prostate surface using learned shape and appearance information from a set of training images. To evaluate the algorithm, we assessed the error metric values in the context of measured inter-observer variability and compared performance to that of our previously published semi-automatic approach. The automatic algorithm needed an average execution time of ∼60 s to segment the prostate in 3D. When compared to a single-observer reference standard, the automatic algorithm has an average mean absolute distance of 2.8 mm, Dice similarity coefficient of 82%, recall of 82%, precision of 84%, and volume difference of 0.5 cm
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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.