Purpose Automated segmentation could improve the efficiency of modeling‐based pelvic
organ prolapse (POP) evaluations. However, segmentation performance is limited by the …

Background Pelvic organ prolapse (POP) is a pelvic floor dysfunction disease which affects
females. The volume of pelvic floor muscle, especially the levator ani muscle (LAM), is an …

Introduction and hypothesis We aimed to develop a deep learning-based multi-label
classification model to simultaneously diagnose three types of pelvic organ prolapse using …

With the increasing usage of radiograph images as a most common medical imaging system
for diagnosis, treatment planning, and clinical studies, it is increasingly becoming a vital …

Pelvic organ prolapse (POP) is a major women's health problem. Its diagnosis through
magnetic resonance imaging (MRI) has become popular due to current inaccuracies of …

Purpose To create a network which fully utilizes multi‐sequence MRI and compares
favorably with manual human contouring. Methods We retrospectively collected 89 MRI …

Pelvic floor disorders mainly affect women and turn to be a public health issue although their
pathophysiology is still poorly understood. As the main concerned organs undergo motions …

Accurate segmentation of pelvic organs (ie, prostate, bladder, and rectum) from CT image is
crucial for effective prostate cancer radiotherapy. However, it is a challenging task due to: 1) …

Levator ani muscle (LAM) avulsion is a common complication of vaginal childbirth and is
linked to several pelvic floor disorders. Diagnosing and treating these conditions require …

Magnetic resonance imaging (MRI) measurements are essential for the diagnosis of pelvic
organ prolapse given the inaccuracy of clinical examination. However, MRI pelvic floor …