Segmentation Based on Anatomical Models

During the past few years, the usage of deformable anatomical atlases has been extensively investigated as an appealing tool for the coding of prior anatomical information for image interpretation. The method is based on a representative deterministic [14] or probabilistic [15] image volume as an anatomical model. For this the actual patient data has to be spatially normalized, thus it has to be mapped onto the template that conforms to the standard anatomical space used by the model. The applied registration procedures range from simple parametric edge matching [16] and rigid registration methods over to increasingly more complex algorithms using affine, projective, and curved transformations. Other methods use complex physically inspired algorithms for elastic deformation or viscous fluid motion [17]. In the latter the transformations are constrained to be consistent with the physical properties of deformable elastic solids or those of viscous fluids. Viscous fluid models are less constraining than linear elastic models and allow long-distance, nonlinear deformations of small subregions. In these formulations, the deformed configuration of the atlas is usually determined by driving the deformation using only pixel-by-pixel intensity similarity between the images if a reasonable level of automation has to be achieved. Common to all registration methods is the resulting dense vector field that defines the mapping of the subject's specific anatomy onto the anatomy template used for the atlas.

The usage of deformable atlases seems to be a very elegant way to use prior anatomical information in segmentation, as it allows to gain support from the success of current image registration research. Once the spatial mapping between the atlas and the individual data has been established, it can be used to transfer all spatially related information predefined on the atlas (as organ labels, functional information, etc.) to the actual patient image.

This approach is, however, fundamentally dependent on the anatomical and physiological validity of the generated mapping. It has to be understood, that a successful warping of one dataset into the other, does not guarantee that it also makes sense as an anatomical mapping. In other words, the fact that the registration result looks perfect offers no guarantee that it makes sense from the anatomical point of view. To warp a leg into a nose is perfectly possible, but will not allow any reasonable physiological interpretation.

To make the results of the registration sensible, i.e., useful for image segmentation, one has to solve the correspondence problem. This means that we have to ensure that the mapping establishes a correspondence between the atlas and the patient, which is physiologically and anatomically meaningful. For the time being, purely intensity driven registration cannot be expected to do so in general. Therefore, in the practice such correspondence usually has to be strongly supported using anatomical landmarks [18,19]. Landmark identification needs, however, in most cases tedious manual work, compromising the quest for automatic procedures. The following section discusses one very popular way to address some of the mentioned fundamental problems of the atlas-based representation of anatomical knowledge. It can, however, hardly be expected that any of the individual methods alone can successfully deal with all aspects of automatic segmentation, and first attempts to combine different approaches have already been published [20].

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