2 March 2007 Automatic whole heart segmentation in CT images: method and validation
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Abstract
Deformable models have already been successfully applied to the semi-automatic segmentation of organs from medical images. We present an approach which enables the fully automatic segmentation of the heart from multi-slice computed tomography images. Compared to other approaches, we address the complete segmentation chain comprising both model initialization and adaptation. A multi-compartment mesh describing both atria, both ventricles, the myocardium around the left ventricle and the trunks of the great vessels is adapted to an image volume. The adaptation is performed in a coarse-to- fine manner by progressively relaxing constraints on the degrees of freedom of the allowed deformations. First, the mesh is translated to a rough estimate of the heart's center of mass. Then, the mesh is deformed under the action of image forces. We first constrain the space of deformations to parametric transformations, compensating for global misalignment of the model chambers. Finally, a deformable adaptation is performed to account for more local and subtle variations of the patient's anatomy. The whole heart segmentation was quantitatively evaluated on 25 volume images and qualitatively validated on 42 clinical cases. Our approach was found to work fully automatically in 90% of cases with a mean surface- to-surface error clearly below 1.0 mm. Qualitatively, expert reviewers rated the overall segmentation quality as 4.2±0.7 on a 5-point scale.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Olivier Ecabert, Olivier Ecabert, Jochen Peters, Jochen Peters, Matthew J. Walker, Matthew J. Walker, Jens von Berg, Jens von Berg, Cristian Lorenz, Cristian Lorenz, Mani Vembar, Mani Vembar, Mark E. Olszewski, Mark E. Olszewski, Jürgen Weese, Jürgen Weese, } "Automatic whole heart segmentation in CT images: method and validation", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65120G (2 March 2007); doi: 10.1117/12.705853; https://doi.org/10.1117/12.705853
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