Comput Math Methods Med - A multiatlas segmentation using graph cuts with applications to liver segmentation in CT scans.

Tópicos

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Resumo

An atlas-based segmentation approach is presented that combines low-level operations, an affine probabilistic atlas, and a multiatlas-based segmentation. The proposed combination provides highly accurate segmentation due to registrations and atlas selections based on the regions of interest (ROIs) and coarse segmentations. Our approach shares the following common elements between the probabilistic atlas and multiatlas segmentation: (a) the spatial normalisation and (b) the segmentation method, which is based on minimising a discrete energy function using graph cuts. The method is evaluated for the segmentation of the liver in computed tomography (CT) images. Low-level operations define a ROI around the liver from an abdominal CT. We generate a probabilistic atlas using an affine registration based on geometry moments from manually labelled data. Next, a coarse segmentation of the liver is obtained from the probabilistic atlas with low computational effort. Then, a multiatlas segmentation approach improves the accuracy of the segmentation. Both the atlas selections and the nonrigid registrations of the multiatlas approach use a binary mask defined by coarse segmentation. We experimentally demonstrate that this approach performs better than atlas selections and nonrigid registrations in the entire ROI. The segmentation results are comparable to those obtained by human experts and to other recently published results.

Resumo Limpo

atlasbas segment approach present combin lowlevel oper affin probabilist atlas multiatlasbas segment propos combin provid high accur segment due registr atlas select base region interest roi coars segment approach share follow common element probabilist atlas multiatla segment spatial normalis b segment method base minimis discret energi function use graph cut method evalu segment liver comput tomographi ct imag lowlevel oper defin roi around liver abdomin ct generat probabilist atlas use affin registr base geometri moment manual label data next coars segment liver obtain probabilist atlas low comput effort multiatla segment approach improv accuraci segment atlas select nonrigid registr multiatla approach use binari mask defin coars segment experiment demonstr approach perform better atlas select nonrigid registr entir roi segment result compar obtain human expert recent publish result

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