RATIONALE AND OBJECTIVES: An atlas-based automated liver segmentation method from three-dimensional computed tomographic (3D CT) images has been developed. The method uses two types of atlases, a probabilistic atlas (PA) and a statistical shape model (SSM). MATERIALS AND METHODS: Voxel-based segmentation with a PA is first performed to obtain a liver region, then the obtained region is used as the initial region for subsequent SSM fitting to 3D CT images. To improve reconstruction accuracy, particularly for highly deformed livers, we use a multilevel SSM (ML-SSM). In ML-SSM, the entire shape is divided into patches, with principal component analysis applied to each patch. To avoid inconsistency among patches, we introduce a new constraint called the "adhesiveness constraint" for overlapping regions among patches. RESULTS: The PA and ML-SSM were constructed from 20 training datasets. We applied the proposed method to eight evaluation datasets. On average, volumetric overlap of 89.2 +/- 1.4% and average distance of 1.36 +/- 0.19 mm were obtained. CONCLUSIONS: The proposed method was shown to improve segmentation accuracy for datasets including highly deformed livers. We demonstrated that segmentation accuracy is improved using the initial region obtained with PA and the introduced constraint for ML-SSM.
RATIONALE AND OBJECTIVES: An atlas-based automated liver segmentation method from three-dimensional computed tomographic (3D CT) images has been developed. The method uses two types of atlases, a probabilistic atlas (PA) and a statistical shape model (SSM). MATERIALS AND METHODS: Voxel-based segmentation with a PA is first performed to obtain a liver region, then the obtained region is used as the initial region for subsequent SSM fitting to 3D CT images. To improve reconstruction accuracy, particularly for highly deformed livers, we use a multilevel SSM (ML-SSM). In ML-SSM, the entire shape is divided into patches, with principal component analysis applied to each patch. To avoid inconsistency among patches, we introduce a new constraint called the "adhesiveness constraint" for overlapping regions among patches. RESULTS: The PA and ML-SSM were constructed from 20 training datasets. We applied the proposed method to eight evaluation datasets. On average, volumetric overlap of 89.2 +/- 1.4% and average distance of 1.36 +/- 0.19 mm were obtained. CONCLUSIONS: The proposed method was shown to improve segmentation accuracy for datasets including highly deformed livers. We demonstrated that segmentation accuracy is improved using the initial region obtained with PA and the introduced constraint for ML-SSM.
Authors: Kenji Suzuki; Ryan Kohlbrenner; Mark L Epstein; Ademola M Obajuluwa; Jianwu Xu; Masatoshi Hori Journal: Med Phys Date: 2010-05 Impact factor: 4.071
Authors: Toshiyuki Okada; Marius George Linguraru; Masatoshi Hori; Ronald M Summers; Noriyuki Tomiyama; Yoshinobu Sato Journal: Med Image Anal Date: 2015-07-04 Impact factor: 8.545