Automated segmentation of the liver from 3D CT images using probabilistic atlas and multi-level statistical shape model

Authors:
Toshiyuki Okada;Ryuji Shimada;Yoshinobu Sato;Masatoshi Hori;Keita Yokota;Masahiko Nakamoto;Yen-Wei Chen;Hironobu Nakamura;Shinichi Tamura
Affiliations:
Division of Image Analysis, Osaka University Graduate School of Medicine, Suita, Osaka, Japan;Division of Image Analysis, Osaka University Graduate School of Medicine, Suita, Osaka, Japan;Division of Image Analysis, Osaka University Graduate School of Medicine, Suita, Osaka, Japan;Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan;Division of Image Analysis, Osaka University Graduate School of Medicine, Suita, Osaka, Japan;Division of Image Analysis, Osaka University Graduate School of Medicine, Suita, Osaka, Japan;College of Information Science and Engineering, Ritsumeikan University, Japan;Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan;Division of Image Analysis, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Year:
2007

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Abstract

An atlas-based automated liver segmentation method from 3D CT images is described. The method utilizes two types of atlases, that is, the probabilistic atlas (PA) and statistical shape model (SSM). Voxel-based segmentation with PA is firstly performed to obtain a liver region, and then the obtained region is used as the initial region for subsequent SSM fitting to 3D CT images. To improve reconstruction accuracy especially for largely deformed livers, we utilize a multi-level SSM (ML-SSM). In ML-SSM, the whole shape is divided into patches, and principal component analysis is applied to each patches. To avoid the inconsistency among patches, we introduce a new constraint called the adhesiveness constraint for overlap regions among patches. In experiments, we demonstrate that segmentation accuracy improved by using the initial region obtained with PA and the introduced constraint for ML-SSM.