ArticleJournal of medical imaging (Bellingham, Wash.)2019
Deep learning-based three-dimensional segmentation of the prostate on computed tomography images.
Article in Journal of medical imaging (Bellingham, Wash.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Technical note: The effect of image annotation with minimal manual interaction for semiautomatic prostate segmentation in CT images using fully convolutional neural networks.Medical physics · 2022Article
- Three-dimensional prostate CT segmentation through fine-tuning of a pre-trained neural network using no reference labeling.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Deep Learning-Based Three-Dimensional Oral Conical Beam Computed Tomography for Diagnosis.Journal of healthcare engineering · 2021Article
- Semi-automated three-dimensional segmentation for cardiac CT images using deep learning and randomly distributed points.Proceedings of SPIE--the International Society for Optical EngineeringArticle
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Authors and funding
5 authors.
Funding
Abstract
Segmentation of the prostate in computed tomography (CT) is used for planning and guidance of prostate treatment procedures. However, due to the low soft-tissue contrast of the images, manual delineation of the prostate on CT is a time-consuming task with high interobserver variability. We developed an automatic, three-dimensional (3-D) prostate segmentation algorithm based on a customized U-Net architecture. Our dataset contained 92 3-D abdominal CT scans from 92 patients, of which 69 images were used for training and validation and the remaining for testing the convolutional neural network model. Compared to manual segmentation by an expert radiologist, our method achieved
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