Prostate segmentation in MRI using fused T2-weighted and elastography images

Guy Nir; Ramin S. Sahebjavaher; Ali Baghani; Ralph Sinkus; Septimiu E. Salcudean

doi:10.1117/12.2043300

21 March 2014 Prostate segmentation in MRI using fused T2-weighted and elastography images

Guy Nir, Ramin S. Sahebjavaher, Ali Baghani, Ralph Sinkus, Septimiu E. Salcudean

Author Affiliations +

Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 90340C (2014) https://doi.org/10.1117/12.2043300
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Segmentation of the prostate in medical imaging is a challenging and important task for surgical planning and delivery of prostate cancer treatment. Automatic prostate segmentation can improve speed, reproducibility and consistency of the process. In this work, we propose a method for automatic segmentation of the prostate in magnetic resonance elastography (MRE) images. The method utilizes the complementary property of the elastogram and the corresponding T2-weighted image, which are obtained from the phase and magnitude components of the imaging signal, respectively. It follows a variational approach to propagate an active contour model based on the combination of region statistics in the elastogram and the edge map of the T2-weighted image. The method is fast and does not require prior shape information. The proposed algorithm is tested on 35 clinical image pairs from five MRE data sets, and is evaluated in comparison with manual contouring. The mean absolute distance between the automatic and manual contours is 1.8mm, with a maximum distance of 5.6mm. The relative area error is 7.6%, and the duration of the segmentation process is 2s per slice.

Citation Download Citation

Guy Nir, Guy Nir, Ramin S. Sahebjavaher, Ramin S. Sahebjavaher, Ali Baghani, Ali Baghani, Ralph Sinkus, Ralph Sinkus, Septimiu E. Salcudean, Septimiu E. Salcudean, "Prostate segmentation in MRI using fused T2-weighted and elastography images", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90340C (21 March 2014); doi: 10.1117/12.2043300; https://doi.org/10.1117/12.2043300

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