The puborectalis muscle (PRM) is the pelvic floor muscle, which encircles the rectum, uretra and vagina. It plays a critical role in maintaining continence, providing support to the pelvic organs and allows normal vaginal childbirth. During childbirth the muscle may have to stretch up to 3 times its original length, which can cause trauma to this muscle. This is the main reason that one in four women above the 40 years old suffers from urinary incontinence and one out of six will have a pelvic organ prolapse. This has a substantial negative influence on physical, emotional and social well-being.
Obtaining functional information about the PRM is crucial in understanding mechanisms associated with pelvic floor (dys)function, and in providing diagnostic and therapeutic important information. Since the PRM is located deep inside the pelvis obtaining direct functional information, like EMG or direct muscle force recordings, is not feasible.
In this project we aim develop functional imaging methods based on deformation (strain) quantification as derived from 3D and 4D ultrasound recordings as already applied in cardiology. Although the PRM can be visualized with 3D and 4D transperineal ultrasound, current post-processing analysis tools only allow us to obtain indirect functional information in a 2-D reconstruction. We are dedicated to unveil the 3D anatomical and functional information about the PRM and to integrate these findings into a reliable clinical workflow. In order to do so we will address the following topics:
1. Development of Automatic Segmentation and Static Tissue Characterization software. This will allow us to reconstruct accurate 3-D models of the PRM and identify and study structural integrity. (RAM, Utwente)
2. Development of Functional Measurements. Strain and tissue elastography measurements will be incorporated to improve diagnosis of pelvic floor (dys)function and guide clinical practice. (MUSIC, Radboud UMC)
3. Clinical Relevance and Validation. The developments under 1 and 2 will be continuously tested on clinical datasets of the target populations during the study period. This will allow direct developmental feedback in general and for specific clinical indications summarized under. (Vrouw en Baby, UMC Utrecht)
In Enschede we will focus on the automatic segmentation and static tissue characterization of the PRM in 3D ultrasound. An automatic segmentation using an active appearance model has already provided promising results. We will look more into automatic segmentation based on deep learning. Also we will improve our training data by looking into the anatomy in more detail. At last more structural information about the PRM will be acquired by static tissue characterization.
If you are interested in a master or bachelor assignment, please contact Frieda van den Noort (firstname.lastname@example.org).