Share Email Print

Proceedings Paper

Deep learning and shapes similarity for joint segmentation and tracing single neurons in SEM images
Author(s): Qiang Rao; Chi Xiao; Hua Han; Xi Chen; Lijun Shen; Qiwei Xie
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Extracting the structure of single neurons is critical for understanding how they function within the neural circuits. Recent developments in microscopy techniques, and the widely recognized need for openness and standardization provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons. In order to look into the fine structure of neurons, we use the Automated Tape-collecting Ultra Microtome Scanning Electron Microscopy (ATUM-SEM) to get images sequence of serial sections of animal brain tissue that densely packed with neurons. Different from other neuron reconstruction method, we propose a method that enhances the SEM images by detecting the neuronal membranes with deep convolutional neural network (DCNN) and segments single neurons by active contour with group shape similarity. We joint the segmentation and tracing together and they interact with each other by alternate iteration that tracing aids the selection of candidate region patch for active contour segmentation while the segmentation provides the neuron geometrical features which improve the robustness of tracing. The tracing model mainly relies on the neuron geometrical features and is updated after neuron being segmented on the every next section. Our method enables the reconstruction of neurons of the drosophila mushroom body which is cut to serial sections and imaged under SEM. Our method provides an elementary step for the whole reconstruction of neuronal networks.

Paper Details

Date Published: 24 February 2017
PDF: 8 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013329 (24 February 2017); doi: 10.1117/12.2254284
Show Author Affiliations
Qiang Rao, Institute of Automation (China)
Chi Xiao, Institute of Automation (China)
Hua Han, Institute of Automation (China)
Ctr. for Excellence in Brain Science and Intelligence Technology (China)
Xi Chen, Institute of Automation (China)
Lijun Shen, Institute of Automation (China)
Qiwei Xie, Institute of Automation (China)

Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

© SPIE. Terms of Use
Back to Top