Fast prostate segmentation for brachytherapy based on joint fusion of images and labels

Saman Nouranian; Mahdi Ramezani; S. Sara Mahdavi; Ingrid Spadinger; William J. Morris; Septimiu E. Salcudean; Purang Abolmaesumi

doi:10.1117/12.2042692

12 March 2014 Fast prostate segmentation for brachytherapy based on joint fusion of images and labels

Saman Nouranian, Mahdi Ramezani, S. Sara Mahdavi, Ingrid Spadinger, William J. Morris, Septimiu E. Salcudean, Purang Abolmaesumi

Author Affiliations +

Proceedings Volume 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling; 90361A (2014) https://doi.org/10.1117/12.2042692
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Brachytherapy as one of the treatment methods for prostate cancer takes place by implantation of radioactive seeds inside the gland. The standard of care for this treatment procedure is to acquire transrectal ultrasound images of the prostate which are segmented in order to plan the appropriate seed placement. The segmentation process is usually performed either manually or semi-automatically and is associated with subjective errors because the prostate visibility is limited in ultrasound images. The current segmentation process also limits the possibility of intra-operative delineation of the prostate to perform real-time dosimetry. In this paper, we propose a computationally inexpensive and fully automatic segmentation approach that takes advantage of previously segmented images to form a joint space of images and their segmentations. We utilize joint Independent Component Analysis method to generate a model which is further employed to produce a probability map of the target segmentation. We evaluate this approach on the transrectal ultrasound volume images of 60 patients using a leave-one-out cross-validation approach. The results are compared with the manually segmented prostate contours that were used by clinicians to plan brachytherapy procedures. We show that the proposed approach is fast with comparable accuracy and precision to those found in previous studies on TRUS segmentation.

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Saman Nouranian, Mahdi Ramezani, S. Sara Mahdavi, Ingrid Spadinger, William J. Morris, Septimiu E. Salcudean, and Purang Abolmaesumi "Fast prostate segmentation for brachytherapy based on joint fusion of images and labels", Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 90361A (12 March 2014); https://doi.org/10.1117/12.2042692

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