Fig. 11.18 Statistical shape model based whole heart segmentation from CT [24], visualized using ITK-SNAP. Top left, top right, bottom right panels: three orthogonal slices through the CT volume. Bottom left panel: surface rendering of segmentation. The four chambers and major vessels of the heart are rendered in different colors: green indicates left ventricle; light brown indicates left ventricular myocardium; dark brown indicates right ventricle; blue indicates right atrium; pink indicates left atrium

11.5.3 Summary

Segmentation is the process of delineating structures of interest from images. This is an important task in medical imaging as it forms a preprocessing step for many operations, such as making quantitative measurements to monitor disease progression. Segmentation can be performed manually, semi-automatically or fully automatically. Manual segmentation can be accurate but time-consuming and prone to observer error or bias. Deformable models can be used for semi-automatic segmentation and one of the most well-known examples is the 2D Snakes algorithm, with Balloons being the analogous process in 3D. In fact, if algorithms for automatic placement of the initial contour/surface are available, deformable model based approaches can sometimes be close to fully automatic. Such algorithms would probably involve some sort of prior knowledge of the anatomy being segmented and, in general, fully automatic approaches make use of such prior knowledge in some