Automated contour tracking and trajectory classification of pelvic organs on dynamic MRI

Iman Nekooeimehr; Susana K. Lai-Yuen; Paul Bao; Alfredo Weitzenfeld; Stuart Hart M.D.

doi:10.1117/1.JMI.5.1.014008

30 March 2018 Automated contour tracking and trajectory classification of pelvic organs on dynamic MRI

Iman Nekooeimehr, Susana K. Lai-Yuen, Paul Bao, Alfredo Weitzenfeld, Stuart Hart M.D.

Author Affiliations +

Journal of Medical Imaging, Vol. 5, Issue 1, 014008 (March 2018). https://doi.org/10.1117/1.JMI.5.1.014008

Abstract

A method is presented to automatically track and segment pelvic organs on dynamic magnetic resonance imaging (MRI) followed by multiple-object trajectory classification to improve understanding of pelvic organ prolapse (POP). POP is a major health problem in women where pelvic floor organs fall from their normal position and bulge into the vagina. Dynamic MRI is presently used to analyze the organs’ movements, providing complementary support for clinical examination. However, there is currently no automated or quantitative approach to measure the movement of the pelvic organs and their correlation with the severity of prolapse. In the proposed method, organs are first tracked and segmented using particle filters and k-means clustering with prior information. Then, the trajectories of the pelvic organs are modeled using a coupled switched hidden Markov model to classify the severity of POP. Results demonstrate that the presented method can automatically track and segment pelvic organs with a Dice similarity index above 78% and Hausdorff distance of <5.2 mm for 94 tested cases while demonstrating correlation between organ movement and POP. This work aims to enable automatic tracking and analysis of multiple deformable structures from images to improve understanding of medical disorders.

Citation Download Citation

Iman Nekooeimehr, Susana K. Lai-Yuen, Paul Bao, Alfredo Weitzenfeld, and Stuart Hart M.D. "Automated contour tracking and trajectory classification of pelvic organs on dynamic MRI," Journal of Medical Imaging 5(1), 014008 (30 March 2018). https://doi.org/10.1117/1.JMI.5.1.014008

Received: 13 October 2017; Accepted: 12 March 2018; Published: 30 March 2018

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