| Abstract |
Segmentation is important to define the spatial extension of body
anatomic structures (objects) in medical images for quantitative
analysis. In this context, it is desirable to eliminate (at least
minimize) user interaction. This aim is feasible by combining object
delineation algorithms with Object Shape Models (OSMs). While the
former can better capture the actual shape of the object in the image,
the latter provides shape constraints to assist its location and
delineation. In this talk, we will review two important classes of
OSMs for medical image segmentation: statistical (SOSMs) and fuzzy
(FOSMs). SOSMs rely on the image mapping onto a reference coordinate
system, which indicates the probability of each voxel to be in the
object (a probabilistic atlas built from a set of training images and
their segmentation masks). Imperfect mappings due to shape and texture
variations asks for object delineation algorithms, but the methods
usually assume that the atlas is at the best position for
delineation. Multiple atlases per object can mitigate the problem and
a recent trend is to use each training mask as an individual atlas. By
mapping them onto the coordinate system of a new image, object
delineation can be accomplished by label fusion. However, the
processing time for deformable registration is critical to make SOSMs
suitable for large scale studies. FOSMs appear as a recent alternative
to avoid reference systems (deformable registration) by translating
the training masks to a common reference point for model
construction. This relaxes the shape constraints, but asks for a more
effective object delineation algorithm and some efficient approach for
object's location. One of the solutions, named optimum object search,
translates the model inside an estimated search region in the image
while a criterion function guides translation and determines the best
delineated object among candidates. This makes segmentation with FOSMs
considerably faster than with SOSMs, but SOSMs that adopt the optimum
object search can be more effective and with less atlases per
object. We will then discuss the pros and cons of the recent FOSM and
SOSM approaches by providing alternative directions, which also
include the user to correct segmentation errors and improve the
models.
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