TY - GEN
T1 - CRF formulation of active contour population for efficient three-dimensional neurite tracing
AU - Gulyanon, S.
AU - Sharifai, N.
AU - Kim, M. D.
AU - Chiba, A.
AU - Tsechpenakis, G.
N1 - Funding Information:
This work is supported by NSF/DBI [#1252597]: 'CAREER: Modeling the structure and dynamics of neuronal circuits in the Drosophila larvae using image analytics' awarded to G. Tsechpenakis
Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/15
Y1 - 2016/6/15
N2 - We present a conditional random field for three-dimensional neurite tracing, using a population of open curve active contours. We aim at increased robustness under spatially varying neurite-background contrast, and at the same time reducing the computational complexity compared to the state-of-the-art. While most existing active contour based methods perform tracing by evolving multiple snakes along the neurite centerline in a sequential manner, our approach implements a simultaneous evolution, reducing the complexity as we show theoretically in our algorithm analysis and experimentally. Our approach provides increased accuracy in ambiguous regions (e.g., low contrast, neurite bifurcations and crossovers, etc.), by exploiting interactions among spatially neighboring snakes. We illustrate the performance of our method and compare it with existing frameworks using sample volumes of wild-type sensory neurons in the larval Drosophila.
AB - We present a conditional random field for three-dimensional neurite tracing, using a population of open curve active contours. We aim at increased robustness under spatially varying neurite-background contrast, and at the same time reducing the computational complexity compared to the state-of-the-art. While most existing active contour based methods perform tracing by evolving multiple snakes along the neurite centerline in a sequential manner, our approach implements a simultaneous evolution, reducing the complexity as we show theoretically in our algorithm analysis and experimentally. Our approach provides increased accuracy in ambiguous regions (e.g., low contrast, neurite bifurcations and crossovers, etc.), by exploiting interactions among spatially neighboring snakes. We illustrate the performance of our method and compare it with existing frameworks using sample volumes of wild-type sensory neurons in the larval Drosophila.
KW - CRF inference
KW - Drosophila
KW - active contours
KW - neurite tracing
UR - http://www.scopus.com/inward/record.url?scp=84978436613&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84978436613&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2016.7493338
DO - 10.1109/ISBI.2016.7493338
M3 - Conference contribution
AN - SCOPUS:84978436613
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 593
EP - 597
BT - 2016 IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
T2 - 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
Y2 - 13 April 2016 through 16 April 2016
ER -