Automated tracking, segmentation and trajectory classification of pelvic organs on dynamic MRI

Abstract

Pelvic organ prolapse is a major health problem in women where pelvic floor organs (bladder, uterus, small bowel, and rectum) fall from their normal position and bulge into the vagina. Dynamic Magnetic Resonance Imaging (DMRI) is presently used to analyze the organs' movements from rest to maximum strain providing complementary support for diagnosis. 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 this paper, a two-stage method is presented to automatically track and segment pelvic organs on DMRI followed by a multiple-object trajectory classification method to improve the diagnosis of pelvic organ prolapse. Organs are first tracked using particle filters and K-means clustering with prior information. Then, they are segmented using the convex hull of the cluster of particles. Finally, the trajectories of the pelvic organs are modeled using a new Coupled Switched Hidden Markov Model (CSHMM) to classify the severity of pelvic organ prolapse. The tracking and segmentation results are validated using Dice Similarity Index (DSI) whereas the classification results are compared with two manual clinical measurements. Results demonstrate that the presented method is able to automatically track and segment pelvic organs with a DSI above 82% for 26 out of 46 cases and DSI above 75% for all 46 tested cases. The accuracy of the trajectory classification model is also better than current manual measurements.

3D Ultrasound Strain Imaging of Puborectalis Muscle

“…These techniques are mainly used to image the anatomy to diagnose POP or SUI. Segmented organs in MRI or US images are used in biomechanical analysis to gain a better understanding of various pelvic organ disorders or specifically to diagnose POP (Akhondi-Asl et al 2014;Onal et al 2014Onal et al , 2016Nekooeimehr et al 2016;Wang et al 2018). Furthermore, biomechanical modeling using imaging as input has been performed to enhance understanding of the anatomy of PF, aid in the diagnosis of various PF disorders and aid in surgical planning for corrective surgeries (Damser et al 2002;Parikh et al 2002;Bellemare et al 2007;Pu et al 2007;Lee et al 2009).…”

A fast 4D IMRT/VMAT planning method based on segment aperture morphing

“…16 Many techniques have been developed for motion compensation using 4D imaging modalities like 4DCT 17 such as creating an internal target volume (ITV) to encompass the entire tumor motion, 3,18,19 gating, 3 and tumor tracking. 3,[20][21][22][23] ITV techniques have the tendency to increase treatment field sizes and thus increase the dose to healthy tissue. Gating techniques can avoid large margins, however, they tend to be inefficient because the treatment beam is only turned on during a fraction of the patient's breathing cycle.…”

“…Intrafractional changes are mostly attributed to breathing motion in the thorax and to respiration and bowel motion in the abdomen . Many techniques have been developed for motion compensation using 4D imaging modalities like 4DCT such as creating an internal target volume (ITV) to encompass the entire tumor motion, gating, and tumor tracking . ITV techniques have the tendency to increase treatment field sizes and thus increase the dose to healthy tissue.…”