Automatic segmentation of subcortical brain structures in MR images using information fusion

Abstract

This paper reports a new automated method for the segmentation of internal cerebral structures using an information fusion technique. The information is provided both by images and expert knowledge, and consists in morphological, topological, and tissue constitution data. All this ambiguous, complementary and redundant information is managed using a three-step fusion scheme based on fuzzy logic. The information is first modeled into a common theoretical frame managing its imprecision and incertitude. The models are then fused and a decision is taken in order to reduce the imprecision and to increase the certainty in the location of the structures. The whole process is illustrated on the segmentation of thalamus, putamen, and head of the caudate nucleus from expert knowledge and magnetic resonance images, in a protocol involving 14 healthy volunteers. The quantitative validation is achieved by comparing computed, manually segmented structures and published data by means of indexes assessing the accuracy of volume estimation and spatial location. Results suggest a consistent volume estimation with respect to the expert quantification and published data, and a high spatial similarity of the segmented and computed structures. This method is generic and applicable to any structure that can be defined by expert knowledge and morphological images.

FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using Large Deformation Diffeomorphic Metric Mapping

“…The hippocampus and amygdala segmentation method by Chupin et al (2007) has been shown to perform equally well on diseased subjects (DSC=0.84), but the disadvantage with this method is it requires some manual interaction to place seed points and define a bounding box and is thus not a fully automated method. Another knowledge-driven approach by (Barra and Boire, 2001) uses fuzzy maps for segmentation and reports higher spatial overlaps (V(A ∩ B)/V(A) = 0.84, 0.88, 0.89 for the caudate, putamen and thalamus), but may require re-definition of the maps when used on atrophic structures, limiting applicability of the method. Methods incorporating decision fusion into multiple template-based label-propagation such as those by (Heckemann et al, 2006 and hippocampus) and (Hammers et al, 2007) (DSC = 0.83 and 0.76 for una3ected and atrophic hippocampi) report very high spatial overlap, however as discussed above, the manual labeling of the multiple templates required for this method is a trade-off and may not always be worthwhile.…”

Agentification of Markov model-based segmentation: Application to magnetic resonance brain scans

“…In addition, we consider two levels of knowledge as regards to MR brain scans; tissue knowledge at a local level, and subcortical brain structure knowledge at a regional level. In general, these two levels are processed independently [14,15]. Rather, we consider that they are linked and must be used in a common setting.…”

“…Atlas warping methods are however time consuming and limited due to inter-subject variability. A recent different way to introduce a priori anatomical knowledge is to describe brain anatomy with generic fuzzy spatial relations [14,15]. We generally consider three kind of spatial relations: distance, symmetry and orientation relations.…”

Geometric strategies for neuroanatomic analysis from MRI

“…Classifying gray and white matter by voxel intensity can incorporate voxel continuity or homogeneity using, for example, Markov random fields (Geman and Geman, 1984;Leahy et al, 1991) to model probabilistic constraints on the image or fuzzy logic (Barra and Boire, 2001). The approach of Wells et al (1994) estimates tissue classes (gray matter, white matter, cerebrospinal fluid (CSF)) while simultaneously estimating the bias field using an expectation-maximization (EM) strategy.…”