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A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling
Author(s): Maysam Shahedi; Martin Halicek; Qinmei Li; Lizhi Liu; Zhenfeng Zhang; Sadhna Verma ; David M. Schuster; Baowei Fei
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Paper Abstract

Segmentation of the prostate in magnetic resonance (MR) images has many applications in image-guided treatment planning and procedures such as biopsy and focal therapy. However, manual delineation of the prostate boundary is a time-consuming task with high inter-observer variation. In this study, we proposed a semiautomated, three-dimensional (3D) prostate segmentation technique for T2-weighted MR images based on shape and texture analysis. The prostate gland shape is usually globular with a smoothly curved surface that could be accurately modeled and reconstructed if the locations of a limited number of well-distributed surface points are known. For a training image set, we used an inter-subject correspondence between the prostate surface points to model the prostate shape variation based on a statistical point distribution modeling. We also studied the local texture difference between prostate and non-prostate tissues close to the prostate surface. To segment a new image, we used the learned prostate shape and texture characteristics to search for the prostate border close to an initially estimated prostate surface. We used 23 MR images for training, and 14 images for testing the algorithm performance. We compared the results to two sets of experts’ manual reference segmentations. The measured mean ± standard deviation of error values for the whole gland were 1.4 ± 0.4 mm, 8.5 ± 2.0 mm, and 86 ± 3% in terms of mean absolute distance (MAD), Hausdorff distance (HDist), and Dice similarity coefficient (DSC). The average measured differences between the two experts on the same datasets were 1.5 mm (MAD), 9.0 mm (HDist), and 83% (DSC). The proposed algorithm illustrated a fast, accurate, and robust performance for 3D prostate segmentation. The accuracy of the algorithm is within the inter-expert variability observed in manual segmentation and comparable to the best performance results reported in the literature.

Paper Details

Date Published: 8 March 2019
PDF: 12 pages
Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109512I (8 March 2019); doi: 10.1117/12.2512282
Show Author Affiliations
Maysam Shahedi, The Univ. of Texas at Dallas (United States)
Martin Halicek, The Univ. of Texas at Dallas (United States)
Emory Univ. and Georgia Institute of Technology (United States)
Qinmei Li, The Univ. of Texas at Dallas (United States)
The Second Affiliated Hospital of Guangzhou Medical Univ. (China)
Lizhi Liu, Sun Yat-sen Univ. Cancer Ctr. (China)
Zhenfeng Zhang, The Second Affiliated Hospital of Guangzhou Medical Univ. (China)
Sadhna Verma , Univ. of Cincinnati Medical Ctr. and Cincinnati VA Hospital (United States)
David M. Schuster, Emory Univ. (United States)
Baowei Fei, The Univ. of Texas at Dallas (United States)
The Univ. of Texas Southwestern Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 10951:
Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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