A 3D neurovascular bundles segmentation method based on MR-TRUS deformable registration

Xiaofeng Yang; Peter Rossi; Ashesh B. Jani; Hui Mao; Tomi Ogunleye; Walter J. Curran; Tian Liu

doi:10.1117/12.2077828

20 March 2015 A 3D neurovascular bundles segmentation method based on MR-TRUS deformable registration

Xiaofeng Yang, Peter Rossi, Ashesh B. Jani, Hui Mao, Tomi Ogunleye, Walter J. Curran, Tian Liu

Proceedings Volume 9413, Medical Imaging 2015: Image Processing; 941319 (2015) https://doi.org/10.1117/12.2077828
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

In this paper, we propose a 3D neurovascular bundles (NVB) segmentation method for ultrasound (US) image by integrating MR and transrectal ultrasound (TRUS) images through MR-TRUS deformable registration. First, 3D NVB was contoured by a physician in MR images, and the 3D MRdefined NVB was then transformed into US images using a MR-TRUS registration method, which models the prostate tissue as an elastic material, and jointly estimates the boundary deformation and the volumetric deformations under the elastic constraint. This technique was validated with a clinical study of 6 patients undergoing radiation therapy (RT) treatment for prostate cancer. The accuracy of our approach was assessed through the locations of landmarks, as well as previous ultrasound Doppler images of patients. MR-TRUS registration was successfully performed for all patients. The mean displacement of the landmarks between the post-registration MR and TRUS images was less than 2 mm, and the average NVB volume Dice Overlap Coefficient was over 89%. This NVB segmentation technique could be a useful tool as we try to spare the NVB in prostate RT, monitor NVB response to RT, and potentially improve post-RT potency outcomes.

Citation Download Citation

Xiaofeng Yang, Peter Rossi, Ashesh B. Jani, Hui Mao, Tomi Ogunleye, Walter J. Curran, and Tian Liu "A 3D neurovascular bundles segmentation method based on MR-TRUS deformable registration", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941319 (20 March 2015); https://doi.org/10.1117/12.2077828

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