title: Fully automated localization of multiple pelvic bone structures on MRI.
abstract: In this paper, we present a fully automated localization method for multiple pelvic bone structures on magnetic resonance images (MRI). Pelvic bone structures are currently identified manually on MRI to identify reference points for measurement and evaluation of pelvic organ prolapse (POP). Given that this is a time-consuming and subjective procedure, there is a need to localize pelvic bone structures without any user interaction. However, bone structures are not easily differentiable from soft tissue on MRI as their pixel intensities tend to be very similar. In this research, we present a model that automatically identifies the bounding boxes of the bone structures on MRI using support vector machines (SVM) based classification and non-linear regression model that captures global and local information. Based on the relative locations of pelvic bones and organs, and local information such as texture features, the model identifies the location of the pelvic bone structures by establishing the association between their locations. Results show that the proposed method is able to locate the bone structures of interest accurately. The pubic bone, sacral promontory, and coccyx were correctly detected (DSI > 0.75) in 92%, 90%, and 88% of the testing images. This research aims to enable accurate, consistent and fully automated identification of pelvic bone structures on MRI to facilitate and improve the diagnosis of female pelvic organ prolapse.
authors.forename: Sinan
authors.initials: S
authors.lastname: Onal
authors.forename: Susana
authors.initials: S
authors.lastname: Lai-Yuen
authors.forename: Paul
authors.initials: P
authors.lastname: Bao
authors.forename: Alfredo
authors.initials: A
authors.lastname: Weitzenfeld
authors.forename: Stuart
authors.initials: S
authors.lastname: Hart
mesh_terms: D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010384:Pelvic Bones / Q000033:anatomy & histology*; D056887:Pelvic Organ Prolapse / Q000175:diagnosis*; D010388:Pelvis / Q000033:anatomy & histology; D012007:Rectum / Q000033:anatomy & histology; D012044:Regression Analysis; D060388:Support Vector Machine; D001743:Urinary Bladder / Q000033:anatomy & histology