TY - GEN
T1 - Automated dense neuronal fiber tracing and connectivity mapping at cellular level
AU - Brattain, Laura J.
AU - Telfer, Brian A.
AU - Samsi, Siddharth
AU - Ku, Taeyun
AU - Choi, Heejin
AU - Chung, Kwanghun
N1 - Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/6/15
Y1 - 2017/6/15
N2 - With the advancement of high throughput and high resolution volumetric brain imaging, there is an unmet need to trace dense neuron fibers and study long-range neuron connectivity. An initial pipeline is described for processing cellular-level neuronal fiber data acquired by a new super resolution imaging method called Magnified Analysis of the Proteome (MAP). First, a multiscale vessel enhancement filter is applied to segment fibers of different diameters. Morphological operations are then employed to extract the fiber centerlines, from which a 3D connectivity map is computed. Applying this approach to an initial data set yielded 2% equal error rate for segmentation and 92% accuracy for end-to-end fiber tracing (22 out of 24 hand-traced fibers). Future work calls for scaling up the algorithm to process much larger brain datasets (terabytes and above) and performing graph-based long-range connectivity analysis. This work has the potential to extend our knowledge on brain networks at the cellular level.
AB - With the advancement of high throughput and high resolution volumetric brain imaging, there is an unmet need to trace dense neuron fibers and study long-range neuron connectivity. An initial pipeline is described for processing cellular-level neuronal fiber data acquired by a new super resolution imaging method called Magnified Analysis of the Proteome (MAP). First, a multiscale vessel enhancement filter is applied to segment fibers of different diameters. Morphological operations are then employed to extract the fiber centerlines, from which a 3D connectivity map is computed. Applying this approach to an initial data set yielded 2% equal error rate for segmentation and 92% accuracy for end-to-end fiber tracing (22 out of 24 hand-traced fibers). Future work calls for scaling up the algorithm to process much larger brain datasets (terabytes and above) and performing graph-based long-range connectivity analysis. This work has the potential to extend our knowledge on brain networks at the cellular level.
UR - http://www.scopus.com/inward/record.url?scp=85023162828&partnerID=8YFLogxK
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U2 - 10.1109/ISBI.2017.7950531
DO - 10.1109/ISBI.2017.7950531
M3 - Conference contribution
AN - SCOPUS:85023162828
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 332
EP - 336
BT - 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PB - IEEE Computer Society
T2 - 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
Y2 - 18 April 2017 through 21 April 2017
ER -