ArticleMedical physics2017
A combined learning algorithm for prostate segmentation on 3D CT images.
Article in Medical physics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 20 citations in OpenAlex.
- Prior guided deep difference meta-learner for fast adaptation to stylized segmentation.Machine learning: science and technology · 2025Article
- Deep learning based automatic segmentation of the Internal Pudendal Artery in definitive radiotherapy treatment planning of localized prostate cancer.Physics and imaging in radiation oncology · 2024Article
- Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions.Diagnostics (Basel, Switzerland) · 2023Review
- CT Male Pelvic Organ Segmentation via Hybrid Loss Network With Incomplete Annotation.IEEE transactions on medical imaging · 2020Article
- CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.Medical image analysis · 2019Article
- Deep learning-based three-dimensional segmentation of the prostate on computed tomography images.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- A semiautomatic algorithm for three-dimensional segmentation of the prostate on CT images using shape and local texture characteristics.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
purposeSegmentation of the prostate on CT images has many applications in the diagnosis and treatment of prostate cancer. Because of the low soft-tissue contrast on CT images, prostate segmentation is a challenging task. A learning-based segmentation method is proposed for the prostate on three-dimensional (3D) CT images.
methodsWe combine population-based and patient-based learning methods for segmenting the prostate on CT images. Population data can provide useful information to guide the segmentation processing. Because of inter-patient variations, patient-specific information is particularly useful to improve the segmentation accuracy for an individual patient. In this study, we combine a population learning method and a patient-specific learning method to improve the robustness of prostate segmentation on CT images. We train a population model based on the data from a group of prostate patients. We also train a patient-specific model based on the data of the individual patient and incorporate the information as marked by the user interaction into the segmentation processing. We calculate the similarity between the two models to obtain applicable population and patient-specific knowledge to compute the likelihood of a pixel belonging to the prostate tissue. A new adaptive threshold method is developed to convert the likelihood image into a binary image of the prostate, and thus complete the segmentation of the gland on CT images.
resultsThe proposed learning-based segmentation algorithm was validated using 3D CT volumes of 92 patients. All of the CT image volumes were manually segmented independently three times by two, clinically experienced radiologists and the manual segmentation results served as the gold standard for evaluation. The experimental results show that the segmentation method achieved a Dice similarity coefficient of 87.18 ± 2.99%, compared to the manual segmentation.
conclusionsBy combining the population learning and patient-specific learning methods, the proposed method is effective for segmenting the prostate on 3D CT images. The prostate CT segmentation method can be used in various applications including volume measurement and treatment planning of the prostate.
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