Assistance to neurosurgical planning: using a fuzzy spatial graph model of the brain for locating anatomical targets in MRI

Alice Villéger; Lemlih Ouchchane; Jean-Jacques Lemaire; Jean-Yves Boire

doi:10.1117/12.710380

26 March 2007 Assistance to neurosurgical planning: using a fuzzy spatial graph model of the brain for locating anatomical targets in MRI

Alice Villéger, Lemlih Ouchchane, Jean-Jacques Lemaire, Jean-Yves Boire

Author Affiliations +

Proceedings Volume 6512, Medical Imaging 2007: Image Processing; 651226 (2007) https://doi.org/10.1117/12.710380
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

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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Alice Villéger, Lemlih Ouchchane, Jean-Jacques Lemaire, and Jean-Yves Boire "Assistance to neurosurgical planning: using a fuzzy spatial graph model of the brain for locating anatomical targets in MRI", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 651226 (26 March 2007); https://doi.org/10.1117/12.710380

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KEYWORDS

Tissues

Magnetic resonance imaging

Fuzzy logic

Image segmentation

Brain

Data modeling

Image processing

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