A combined learning algorithm for prostate segmentation on 3D CT images. Issue 11 (22nd September 2017)

• Record Type: Journal Article
• Authors: Ma, Ling; Guo, Rongrong; Zhang, Guoyi; Schuster, David M.; Fei, Baowei
• Is Part Of: Medical physics. Volume 44:Issue 11(2017)
• Page Start: 5768
• Page End: 5781
• Publication Date: 2017-09-22
• Subjects: computed tomography -- image segmentation -- population‐based learning -- prostate; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.12528
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Segmentation 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. Methods: We 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. Results: The 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. Conclusions: By 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.