Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images

Abstract : Segmenting the heart in medical images is a challenging and important task for many applications. In particular, segmenting the heart in CT images is very useful for cardiology and oncological applications such as radiotherapy. Although the majority of methods in the literature are designed for ventricle segmentation, there is a real interest in segmenting the heart as a whole in this modality. In this paper, we address this problem and propose an automatic and robust method, based on anatomical knowledge about the heart, in particular its position with respect to the lungs. This knowledge is represented in a fuzzy formalism and it is used both to define a region of interest and to drive the evolution of a deformable model in order to segment the heart inside this region. The proposed method has been applied on non-contrast CT images and the obtained results have been compared to manual segmentations of the heart, showing the good accuracy and high robustness of our approach.
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A. Moreno, C.M. Takemura, O. Colliot, O. Camara, I. Bloch. Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images. Pattern Recognition, Elsevier, 2008, 41 (8), ⟨10.1016/j.patcog.2008.01.020⟩. ⟨hal-01251275⟩

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