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.
Description
In this paper, we present a tracking approach based on particle filtering, also known as sequential Monte Carlo tracking. Tracking has also been used successfully for segmentation of tubular structures. Most vessel tracking methods model the state as a cross-sectional ellipse or as a cylindroid. In tracking nerve bundles, the regions of low contrast require the state to capture substantially longer segments of the track than what is represented by a cross-section. In addition, nerves tend to change direction, often sharply, which necessitates a use of more complex descriptors than cylinders.
To address the challenges of nerve tracking, we define a flexible particle representation that captures the geometric behavior of the nerve bundles. We use a Bezier spline centerline with a linear radius function to characterize a nerve bundle. We devise a dynamics model for particle updates that encourages continuity and smoothness. Furthermore, we define an image likelihood model that compares gradient fields and intensities of predicted patches with image observations to evaluate a posterior distribution of the particles' importance. Once tracking is completed, we remove spurious segmentations by measuring the quality of the entire tract. We demonstrate successful segmentations of neural tracts and evaluate them relative to expert manual segmentations. To the best of our knowledge, this is the first automatic segmentation of nerve bundles and ganglia.
Results
We compare our results
Conclusion
Literature
Key Investigators
- MIT: Adrian Dalca, Polina Golland
- BWH: Giovanna Danagoulian, Ehud Schmidt
