Int J Comput Assist Radiol Surg - Liver segmentation for contrast-enhanced MR images using partitioned probabilistic model.

Tópicos

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Resumo

RPOSE: Liver volume segmentation is important in computer assisted diagnosis and therapy planning of liver tumors. Manual segmentation is time-consuming, tedious and error prone, so automated methods are needed. Automatic segmentation of MR images is more challenging than for CT images, so a robust system was developed.METHODS: An intensity-based segmentation method that uses probabilistic model to increase the precision of the segmentation was developed. The model was build based on 60 manually contoured liver CT exams and partitioned into 8 parts according to the (Couinaud) segmental anatomy of the liver. The partitioning allows using different intensity statistics in different parts of the organ, which makes it insensitive to local intensity differences from MR artifacts or pathology. The method employs a modality independent model with registration that exploits some LAVA image characteristics. This dependence can be eliminated to adapt the segmentation method for a wide range of MR images.RESULTS: The method was evaluated using eight representative, manually segmented MR LAVA exams. The results show that the method can accurately segment the liver volume despite various MR artifacts and pathology. The evaluation shows that the proposed method provides more precise segmentation (6% average absolute relative volume error) compared with global intensity statistics for the whole organ (20% average absolute relative volume error). The compute time of the method was 30 s in average, which is acceptable for wide range of clinical applications.CONCLUSION: An automatic method that can segment the liver in contrast-enhanced MR LAVA images was developed and tested. The results demonstrate that the method is feasible, efficient and robust to artifacts and pathology.

Resumo Limpo

rpose liver volum segment import comput assist diagnosi therapi plan liver tumor manual segment timeconsum tedious error prone autom method need automat segment mr imag challeng ct imag robust system developedmethod intensitybas segment method use probabilist model increas precis segment develop model build base manual contour liver ct exam partit part accord couinaud segment anatomi liver partit allow use differ intens statist differ part organ make insensit local intens differ mr artifact patholog method employ modal independ model registr exploit lava imag characterist depend can elimin adapt segment method wide rang mr imagesresult method evalu use eight repres manual segment mr lava exam result show method can accur segment liver volum despit various mr artifact patholog evalu show propos method provid precis segment averag absolut relat volum error compar global intens statist whole organ averag absolut relat volum error comput time method s averag accept wide rang clinic applicationsconclus automat method can segment liver contrastenhanc mr lava imag develop test result demonstr method feasibl effici robust artifact patholog

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