KEYWORDS: Image segmentation, 3D image processing, 3D modeling, Ultrasonography, Performance modeling, Education and training, Data modeling, Diseases and disorders, Deep learning, 3D metrology
Three-dimensional (3D) transperineal ultrasound (TPUS) is a valuable imaging tool for evaluating patients with a variety of pelvic floor disorders, including pelvic organ prolapse (POP). Patients with POP have abnormal descent of one or more pelvic organs (i.e., bladder, uterus, vagina) through the levator hiatus, which is often experienced by the patient as a persistent bothersome bulge protruding from the vaginal opening. The enlargement of the hiatal opening measured in the plane of minimal hiatal dimensions (PMHD), has been used as an indication for POP severity. Manually measuring the size of the levator hiatus in 3D TPUS images can be challenging and requires expertise and training and is timeconsuming. Hence a fully automated method for estimating the dimensions of hiatal opening is highly desirable. To this end, we developed a fully automated method to segment the levator hiatus from the PMHD based on the nnU-Net model framework. We trained, validated, and tested on a total of 252 3D US images from 138 patients that may have POP as determined by the pelvic organ prolapse quantification (POP-Q) system. As a benchmark comparison, we compared the nnU-Net to a vanilla U-Net whose hyperparameters were manually tuned. Model performance was determined using Dice similarity coefficient (DSC) and levator hiatus width, length, and area by comparing the model segmentations to manual segmentations. The nnU-Net achieved a DSC of 93.1%±3.3%, absolute width difference of 2.3mm±1.7mm, absolute length difference of 2.6mm±2.5mm and absolute area difference of 1.8cm2±1.3cm2.
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