Assistance to neurosurgical planning: using a fuzzy spatial graph model of the brain for locating anatomical targets in MRI
2007
Symptoms of neurodegenerative pathologies such as Parkinson's disease can be relieved through Deep Brain
Stimulation. This neurosurgical technique relies on high precision positioning of electrodes in specific areas of
the basal ganglia and the thalamus. These subcortical anatomical targets must be located at pre-operative stage,
from a set of MRI acquired under stereotactic conditions. In order to assist surgical planning, we designed a
semi-automated image analysis process for extracting anatomical areas of interest.
Complementary information, provided by both patient's data and expert knowledge, is represented as fuzzy
membership maps, which are then fused by means of suitable possibilistic operators in order to achieve the
segmentation of targets. More specifically, theoretical prior knowledge on brain anatomy is modelled within
a 'virtual atlas' organised as a spatial graph: a list of vertices linked by edges, where each vertex represents
an anatomical structure of interest and contains relevant information such as tissue composition, whereas each
edge represents a spatial relationship between two structures, such as their relative directions. The model is
built using heterogeneous sources of information such as qualitative descriptions from the expert, or quantitative
information from prelabelled images.
For each patient, tissue membership maps are extracted from MR data through a classification step. Prior
model and patient's data are then matched by using a research algorithm (or 'strategy') which simultaneously
computes an estimation of the location of every structures. The method was tested on 10 clinical images,
with promising results. Location and segmentation results were statistically assessed, opening perspectives for
enhancements.
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