IEEE J Biomed Health Inform - Automated Localization of Multiple Pelvic Bone Structures on MRI.

Tópicos

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Resumo

In this paper, we present a fully automated localization method for multiple pelvic bone structures on magnetic resonance images (MRI). Pelvic bone structures are at present identified manually on MRI to locate 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 automatically. However, bone structures are not easily differentiable from soft tissue on MRI as their pixel intensities tend to be very similar. In this paper, we present a model that combines support vector machines (SVM) and non-linear regression capturing global and local information to automatically identify the bounding boxes of bone structures on MRI. The model identifies the location of the pelvic bone structures by establishing the association between their relative locations and using local information such as texture features. Results show that the proposed method is able to locate the bone structures of interest accurately (Dice Similarity Index > 0.75) in 87-91% of the images. This research aims to enable accurate, consistent and fully automated localization of bone structures on MRI to facilitate and improve the diagnosis of health conditions such as female pelvic organ prolapse.

Resumo Limpo

paper present fulli autom local method multipl pelvic bone structur magnet reson imag mri pelvic bone structur present identifi manual mri locat refer point measur evalu pelvic organ prolaps pop given timeconsum subject procedur need local pelvic bone structur automat howev bone structur easili differenti soft tissu mri pixel intens tend similar paper present model combin support vector machin svm nonlinear regress captur global local inform automat identifi bound box bone structur mri model identifi locat pelvic bone structur establish associ relat locat use local inform textur featur result show propos method abl locat bone structur interest accur dice similar index imag research aim enabl accur consist fulli autom local bone structur mri facilit improv diagnosi health condit femal pelvic organ prolaps

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