ArticleProceedings of SPIE--the International Society for Optical Engineering2018
A semiautomatic algorithm for three-dimensional segmentation of the prostate on CT images using shape and local texture characteristics.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Optimization-Incorporated Deep Learning Strategy to Automate L3 Slice Detection and Abdominal Segmentation in Computed Tomography.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment.Quantitative imaging in medicine and surgery · 2023Article
- 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
- Abdominal muscle segmentation from CT using a convolutional neural network.Proceedings of SPIE--the International Society for Optical Engineering · 2020Article
- Deep learning-based three-dimensional segmentation of the prostate on computed tomography images.Journal of medical imaging (Bellingham, Wash.) · 2019Article
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Authors and funding
9 authors.
Funding
Abstract
Prostate segmentation in computed tomography (CT) images is useful for planning and guidance of the diagnostic and therapeutic procedures. However, the low soft-tissue contrast of CT images makes the manual prostate segmentation a time-consuming task with high inter-observer variation. We developed a semi-automatic, three-dimensional (3D) prostate segmentation algorithm using shape and texture analysis and have evaluated the method against manual reference segmentations. In a training data set we defined an inter-subject correspondence between surface points in the spherical coordinate system. We applied this correspondence to model the globular and smoothly curved shape of the prostate with 86, well-distributed surface points using a point distribution model that captures prostate shape variation. We also studied the local texture difference between prostate and non-prostate tissues close to the prostate surface. For segmentation, we used the learned shape and texture characteristics of the prostate in CT images and we used a set of user inputs for prostate localization. We trained our algorithm using 23 CT images and tested it on 10 images. We evaluated the results compared with those of two experts' manual reference segmentations using different error metrics. The average measured Dice similarity coefficient (DSC) and mean absolute distance (MAD) were 88 ± 2% and 1.9 ± 0.5 mm, respectively. The averaged inter-expert difference measured on the same dataset was 91 ± 4% (DSC) and 1.3 ± 0.6 mm (MAD). With no prior intra-patient information, the proposed algorithm showed a fast, robust and accurate performance for 3D CT segmentation.
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Registered trials
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