Comput Methods Programs Biomed - Automated PET-guided liver segmentation from low-contrast CT volumes using probabilistic atlas.

Tópicos

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Resumo

The use of the functional PET information from PET-CT scans to improve liver segmentation from low-contrast CT data is yet to be fully explored. In this paper, we fully utilize PET information to tackle challenging liver segmentation issues including (1) the separation and removal of the surrounding muscles from liver region of interest (ROI), (2) better localization and mapping of the probabilistic atlas onto the low-contrast CT for a more accurate tissue classification, and (3) an improved initial estimation of the liver ROI to speed up the convergence of the expectation-maximization (EM) algorithm for the Gaussian distribution mixture model under the guidance of a probabilistic atlas. The primary liver extraction from the PET volume provides a simple mechanism to avoid the complicated pre-processing of feature extraction as used in the existing liver CT segmentation methods. It is able to guide the probabilistic atlas to better conform to the CT liver region and hence helps to overcome the challenge posed by liver shape variability. Our proposed method was evaluated against manual segmentation by experienced radiologists. Experimental results on 35 clinical PET-CT studies demonstrated that our method is accurate and robust in automated normal liver segmentation.

Resumo Limpo

use function pet inform petct scan improv liver segment lowcontrast ct data yet fulli explor paper fulli util pet inform tackl challeng liver segment issu includ separ remov surround muscl liver region interest roi better local map probabilist atlas onto lowcontrast ct accur tissu classif improv initi estim liver roi speed converg expectationmaxim em algorithm gaussian distribut mixtur model guidanc probabilist atlas primari liver extract pet volum provid simpl mechan avoid complic preprocess featur extract use exist liver ct segment method abl guid probabilist atlas better conform ct liver region henc help overcom challeng pose liver shape variabl propos method evalu manual segment experienc radiologist experiment result clinic petct studi demonstr method accur robust autom normal liver segment

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