| Title: |
Automated Neuron Shape Analysis from Electron Microscopy |
| Authors: |
Seshamani, Sharmishtaa; Elabbady, Leila; Schneider-Mizell, Casey; Mahalingam, Gayathri; Dorkenwald, Sven; Bodor, Agnes; Macrina, Thomas; Bumbarger, Daniel; Buchanan, JoAnn; Takeno, Marc; Yin, Wenjing; Brittain, Derrick; Torres, Russel; Kapner, Daniel; lee, Kisuk; Lu, Ran; Wu, Jinpeng; daCosta, Nuno; Reid, Clay; Collman, Forrest |
| Publication Year: |
2020 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computer Vision and Pattern Recognition; I.4.9; I.5.3; J.3 |
| Description: |
Morphology based analysis of cell types has been an area of great interest to the neuroscience community for several decades. Recently, high resolution electron microscopy (EM) datasets of the mouse brain have opened up opportunities for data analysis at a level of detail that was previously impossible. These datasets are very large in nature and thus, manual analysis is not a practical solution. Of particular interest are details to the level of post synaptic structures. This paper proposes a fully automated framework for analysis of post-synaptic structure based neuron analysis from EM data. The processing framework involves shape extraction, representation with an autoencoder, and whole cell modeling and analysis based on shape distributions. We apply our novel framework on a dataset of 1031 neurons obtained from imaging a 1mm x 1mm x 40 micrometer volume of the mouse visual cortex and show the strength of our method in clustering and classification of neuronal shapes.; Comment: 9 pages, 4 figures |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2006.00100 |
| Accession Number: |
edsarx.2006.00100 |
| Database: |
arXiv |