Projects:NerveSegmentation
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Nerve Segmentation
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bezier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking when compared to expert manual segmentation.
Introduction
Mapping and localization of neural pathways is essential for diagnosis of spinal pathologies, treatment planning, and image-guided interventions. Recent developments in high-resolution MRI have enabled visualization of the nerve bundles as they proceed through the foramen and exit the spinal canal. The neural tracts exhibit good contrast with fluids and bone, but are often of similar intensity to that of marrow and muscle. Manual segmentation of the nerve tracts and ganglia is quite challenging and time-consuming. In this paper, we develop and demonstrate a method for automatic segmentation of neural tracts and ganglia in high-resolution MRI that requires minimal input from an expert.
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Key Investigators
- MIT: Adrian Dalca, Polina Golland
- BWH: Giovanna Danagoulian, Ehud Schmidt
