A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling

Maysam Shahedi; Martin Halicek; Qinmei Li; Lizhi Liu; Zhenfeng Zhang; Sadhna Verma; David M. Schuster; Baowei Fei

doi:10.1117/12.2512282

8 March 2019 A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling

Maysam Shahedi, Martin Halicek, Qinmei Li, Lizhi Liu, Zhenfeng Zhang, Sadhna Verma , David M. Schuster, Baowei Fei

Author Affiliations +

Proceedings Volume 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling; 109512I (2019) https://doi.org/10.1117/12.2512282
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Citation Download Citation

Maysam Shahedi, Martin Halicek, Qinmei Li, Lizhi Liu, Zhenfeng Zhang, Sadhna Verma , David M. Schuster, and Baowei Fei "A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling", Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109512I (8 March 2019); https://doi.org/10.1117/12.2512282

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available