Neurite Tracing with Object Process

Sreetama Basu, Wei Tsang Ooi, Daniel Racoceanu

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

14 Citas (Scopus)


In this paper we present a pipeline for automatic analysis of neuronal morphology: from detection, modeling to digital reconstruction. First, we present an automatic, unsupervised object detection framework using stochastic marked point process. It extracts connected neuronal networks by fitting special configuration of marked objects to the centreline of the neurite branches in the image volume giving us position, local width and orientation information. Semantic modeling of neuronal morphology in terms of critical nodes like bifurcations and terminals, generates various geometric and morphology descriptors such as branching index, branching angles, total neurite length, internodal lengths for statistical inference on characteristic neuronal features. From the detected branches we reconstruct neuronal tree morphology using robust and efficient numerical fast marching methods. We capture a mathematical model abstracting out the relevant position, shape and connectivity information about neuronal branches from the microscopy data into connected minimum spanning trees. Such digital reconstruction is represented in standard SWC format, prevalent for archiving, sharing, and further analysis in the neuroimaging community. Our proposed pipeline outperforms state of the art methods in tracing accuracy and minimizes the subjective variability in reconstruction, inherent to semi-automatic methods.

Idioma originalInglés
Número de artículo7373641
Páginas (desde-hasta)1443-1451
Número de páginas9
PublicaciónIEEE Transactions on Medical Imaging
EstadoPublicada - jun. 2016
Publicado de forma externa


Profundice en los temas de investigación de 'Neurite Tracing with Object Process'. En conjunto forman una huella única.

Citar esto