TY - JOUR
T1 - Automated 3-D neuron tracing with precise branch erasing and confidence controlled back tracking
AU - Liu, Siqi
AU - Zhang, Donghao
AU - Song, Yang
AU - Peng, Hanchuan
AU - Cai, Weidong
N1 - Funding Information:
Manuscript received March 27, 2018; revised April 25, 2018; accepted April 29, 2018. Date of publication May 4, 2018; date of current version October 29, 2018. This work was supported by the Allen Institute for Brain Science. The work of S. Liu was supported in part by the Australian Post-graduate Award Scholarship and in part by the Google Ph.D. Fellowship in Computational Neuroscience. (Corresponding author: Siqi Liu.) S. Liu, D. Zhang, Y. Song, and W. Cai are with the School of Information Technologies, The University of Sydney, Darlington, NSW 2008, Australia (e-mail: [email protected]).
Publisher Copyright:
© 1982-2012 IEEE.
PY - 2018/11
Y1 - 2018/11
N2 - The automatic reconstruction of single neurons from microscopic images is essential to enable large-scale data-driven investigations in neuron morphology research. However, few previous methods were able to generate satisfactory results automatically from 3-D microscopic images without human intervention. In this paper, we developed a new algorithm for automatic 3-D neuron reconstruction. The main idea of the proposed algorithm is to iteratively track backward from the potential neuronal termini to the soma centre. An online confidence score is computed to decide if a tracing iteration should be stopped and discarded from the final reconstruction. The performance improvements comparing with the previous methods are mainly introduced by a more accurate estimation of the traced area and the confidence controlled back-tracking algorithm. The proposed algorithm supports large-scale batch-processing by requiring only one user specified parameter for background segmentation. We bench tested the proposed algorithm on the images obtained from both the DIADEM challenge and the BigNeuron challenge. Our proposed algorithm achieved the state-of-the-art results.
AB - The automatic reconstruction of single neurons from microscopic images is essential to enable large-scale data-driven investigations in neuron morphology research. However, few previous methods were able to generate satisfactory results automatically from 3-D microscopic images without human intervention. In this paper, we developed a new algorithm for automatic 3-D neuron reconstruction. The main idea of the proposed algorithm is to iteratively track backward from the potential neuronal termini to the soma centre. An online confidence score is computed to decide if a tracing iteration should be stopped and discarded from the final reconstruction. The performance improvements comparing with the previous methods are mainly introduced by a more accurate estimation of the traced area and the confidence controlled back-tracking algorithm. The proposed algorithm supports large-scale batch-processing by requiring only one user specified parameter for background segmentation. We bench tested the proposed algorithm on the images obtained from both the DIADEM challenge and the BigNeuron challenge. Our proposed algorithm achieved the state-of-the-art results.
KW - 3-D neuron reconstruction
KW - neuron morphology
UR - http://www.scopus.com/inward/record.url?scp=85046467731&partnerID=8YFLogxK
U2 - 10.1109/TMI.2018.2833420
DO - 10.1109/TMI.2018.2833420
M3 - Article
C2 - 29993997
AN - SCOPUS:85046467731
SN - 0278-0062
VL - 37
SP - 2441
EP - 2452
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 11
M1 - 8354803
ER -