Automated liver segmentation using a normalized probabilistic atlas

Marius George Linguraru; Zhixi Li; Furhawn Shah; See Chin; Ronald M. Summers

doi:10.1117/12.810938

27 February 2009 Automated liver segmentation using a normalized probabilistic atlas

Marius George Linguraru, Zhixi Li, Furhawn Shah, See Chin, Ronald M. Summers

Author Affiliations +

Proceedings Volume 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging; 72622R (2009) https://doi.org/10.1117/12.810938
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Probabilistic atlases of anatomical organs, especially the brain and the heart, have become popular in medical image analysis. We propose the construction of probabilistic atlases which retain structural variability by using a size-preserving modified affine registration. The organ positions are modeled in the physical space by normalizing the physical organ locations to an anatomical landmark. In this paper, a liver probabilistic atlas is constructed and exploited to automatically segment liver volumes from abdominal CT data. The atlas is aligned with the patient data through a succession of affine and non-linear registrations. The overlap and correlation with manual segmentations are 0.91 (0.93 DICE coefficient) and 0.99 respectively. Little work has taken place on the integration of volumetric measures of liver abnormality to clinical evaluations, which rely on linear estimates of liver height. Our application measures the liver height at the mid-hepatic line (0.94 correlation with manual measurements) and indicates that its combination with volumetric estimates could assist the development of a noninvasive tool to assess hepatomegaly.

Citation Download Citation

Marius George Linguraru, Zhixi Li, Furhawn Shah, See Chin, and Ronald M. Summers "Automated liver segmentation using a normalized probabilistic atlas", Proc. SPIE 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 72622R (27 February 2009); https://doi.org/10.1117/12.810938

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