Segmentation of liver portal veins by global optimization

Pieter Bruyninckx; Dirk Loeckx; Dirk Vandermeulen; Paul Suetens

doi:10.1117/12.843995

9 March 2010 Segmentation of liver portal veins by global optimization

Pieter Bruyninckx, Dirk Loeckx, Dirk Vandermeulen, Paul Suetens

Proceedings Volume 7624, Medical Imaging 2010: Computer-Aided Diagnosis; 76241Z (2010) https://doi.org/10.1117/12.843995
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

We present an algorithm for the segmentation of the liver portal veins from an arterial phase CT. The developed segmentation algorithm incorporates a physiological model that states that the vasculature pattern is organized such that the whole organ is perfused using minimal mechanical energy. This model is, amongst others, applicable to the lungs, the liver, and the kidneys. The algorithm first locally detects probable candidate vessel segments in the image. The subset of these segments that generates the most probable vessel tree according the image and the physiological model is afterwards sought by a global optimization method. The algorithm has already been applied successfully to segment heavily simplified lung vessel trees from CT images. Now the general feasibility of this approach is evaluated by applying it to the segmentation of the liver portal veins from an arterial phase CT scan. This is more challenging, because the intensity difference between the vessels and the parenchyma is small. To cope with the low contrast a support vector machines approach with a robust feature vector is used to locally detect vessels. This approach has been applied to a set of five images, for which a ground truth segmentation is available. This algorithm is a first step towards an automatic segmentation of all of the liver vasculature.

Citation Download Citation

Pieter Bruyninckx, Dirk Loeckx, Dirk Vandermeulen, and Paul Suetens "Segmentation of liver portal veins by global optimization", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76241Z (9 March 2010); https://doi.org/10.1117/12.843995

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