title: Tubularity flow field--a technique for automatic neuron segmentation.
abstract: A segmentation framework is proposed to trace neurons from confocal microscopy images. With an increasing demand for high throughput neuronal image analysis, we propose an automated scheme to perform segmentation in a variational framework. Our segmentation technique, called tubularity flow field (TuFF) performs directional regional growing guided by the direction of tubularity of the neurites. We further address the problem of sporadic signal variation in confocal microscopy by designing a local attraction force field, which is able to bridge the gaps between local neurite fragments, even in the case of complete signal loss. Segmentation is performed in an integrated fashion by incorporating the directional region growing and the attraction force-based motion in a single framework using level sets. This segmentation is accomplished without manual seed point selection; it is automated. The performance of TuFF is demonstrated over a set of 2D and 3D confocal microscopy images where we report an improvement of >75% in terms of mean absolute error over three extensively used neuron segmentation algorithms. Two novel features of the variational solution, the evolution force and the attraction force, hold promise as contributions that can be employed in a number of image analysis applications.
authors.forename: Suvadip
authors.initials: S
authors.lastname: Mukherjee
authors.forename: Barry
authors.initials: B
authors.lastname: Condron
authors.forename: Scott T
authors.initials: ST
authors.lastname: Acton
mesh_terms: D000465:Algorithms; D000818:Animals; D016208:Databases, Factual; D004330:Drosophila / Q000033:anatomy & histology; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018613:Microscopy, Confocal / Q000379:methods*; D009474:Neurons / Q000648:ultrastructure*