Int J Comput Assist Radiol Surg - Automatic 3D segmentation of individual facial muscles using unlabeled prior information.

Tópicos

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Resumo

RPOSE: Segmentation of facial soft tissues is required for surgical planning and evaluation, but this is laborious using manual methods and has been difficult to achieve with digital segmentation methods. A new automatic 3D segmentation method for facial soft tissues in magnetic resonance imaging (MRI) images was designed, implemented, and tested.METHODS: A region growing algorithm based on local energy functions, using intensity similarities among neighboring regions as criteria, was developed. This local energy function includes the neighborhood relationships not only in the same dataset but from other training datasets. This approach differs from previous studies where the prior information was represented as manual segmented atlases. In this study, a consensus of many datasets, none of which was labeled a priori, is used to guide the segmentation. The method was tested in MRI scans for adult facial structures. MRI scans were obtained from the Alzheimer's disease neuroimaging initiative database. Comparison was made to results from expert manual segmentation and region growing techniques.RESULTS: The volumetric overlap between automated 3D segmentation results and the ground truth was 82.6% for masseter and 78.8% for temporalis tissues.CONCLUSION: A new automated method to segment various facial soft tissues was implemented and the results compared with standard region growing results. The proposed method shows 3.9% improvement in accuracy over the standard method. Reduction in segmentation errors was consistently achieved in MRI scans.

Resumo Limpo

rpose segment facial soft tissu requir surgic plan evalu labori use manual method difficult achiev digit segment method new automat d segment method facial soft tissu magnet reson imag mri imag design implement testedmethod region grow algorithm base local energi function use intens similar among neighbor region criteria develop local energi function includ neighborhood relationship dataset train dataset approach differ previous studi prior inform repres manual segment atlas studi consensus mani dataset none label priori use guid segment method test mri scan adult facial structur mri scan obtain alzheim diseas neuroimag initi databas comparison made result expert manual segment region grow techniquesresult volumetr overlap autom d segment result ground truth masset temporali tissuesconclus new autom method segment various facial soft tissu implement result compar standard region grow result propos method show improv accuraci standard method reduct segment error consist achiev mri scan

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