Fully automated 3D prostate central gland segmentation in MR images: a LOGISMOS based approach

Yin Yin; Sergei V. Fotin; Senthil Periaswamy; Justin Kunz; Hrishikesh Haldankar; Naira Muradyan; Baris Turkbey; Peter Choyke

doi:10.1117/12.911778

14 February 2012 Fully automated 3D prostate central gland segmentation in MR images: a LOGISMOS based approach

Yin Yin, Sergei V. Fotin, Senthil Periaswamy, Justin Kunz, Hrishikesh Haldankar, Naira Muradyan, Baris Turkbey, Peter Choyke

Author Affiliations +

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 83143B (2012) https://doi.org/10.1117/12.911778
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

One widely accepted classification of a prostate is by a central gland (CG) and a peripheral zone (PZ). In some clinical applications, separating CG and PZ from the whole prostate is useful. For instance, in prostate cancer detection, radiologist wants to know in which zone the cancer occurs. Another application is for multiparametric MR tissue characterization. In prostate T2 MR images, due to the high intensity variation between CG and PZ, automated differentiation of CG and PZ is difficult. Previously, we developed an automated prostate boundary segmentation system, which tested on large datasets and showed good performance. Using the results of the pre-segmented prostate boundary, in this paper, we proposed an automated CG segmentation algorithm based on Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces (LOGISMOS). The designed LOGISMOS model contained both shape and topology information during deformation. We generated graph cost by training classifiers and used coarse-to-fine search. The LOGISMOS framework guarantees optimal solution regarding to cost and shape constraint. A five-fold cross-validation approach was applied to training dataset containing 261 images to optimize the system performance and compare with a voxel classification based reference approach. After the best parameter settings were found, the system was tested on a dataset containing another 261 images. The mean DSC of 0.81 for the test set indicates that our approach is promising for automated CG segmentation. Running time for the system is about 15 seconds.

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Yin Yin, Sergei V. Fotin, Senthil Periaswamy, Justin Kunz, Hrishikesh Haldankar, Naira Muradyan, Baris Turkbey, and Peter Choyke "Fully automated 3D prostate central gland segmentation in MR images: a LOGISMOS based approach", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83143B (14 February 2012); https://doi.org/10.1117/12.911778

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