Automated Axon Segmentation from Highly Noisy Microscopic Videos
Abstract
We present a novel method for automated segmentation of axons in extremely noisy videos obtained via two-photon microscopy in awake mice. We formulate segmentation as a pixel-wise classification problem in which a pixel is classified into "axon" or "non-axon" based on its feature vector. In order to deal with high levels of noise, the features of our classifier are derived from spatio-temporal Independent Component Analysis (stICA) which effectively isolates noise from signal components while leveraging temporal coherence from the video. We fit parametric models to represent the distribution of the extracted features and apply a probabilistic classifier over stICA components to determine the label of each pixel. Finally, we show compelling qualitative and quantitative results from very challenging two-photon microscopic, demonstrating the usefulness of our approach. An example time-series of two-photon images with our automated ROI extraction over layed is available with the supplemental materials.
Cite
Text
Bowler et al. "Automated Axon Segmentation from Highly Noisy Microscopic Videos." IEEE/CVF Winter Conference on Applications of Computer Vision, 2015. doi:10.1109/WACV.2015.126Markdown
[Bowler et al. "Automated Axon Segmentation from Highly Noisy Microscopic Videos." IEEE/CVF Winter Conference on Applications of Computer Vision, 2015.](https://mlanthology.org/wacv/2015/bowler2015wacv-automated/) doi:10.1109/WACV.2015.126BibTeX
@inproceedings{bowler2015wacv-automated,
title = {{Automated Axon Segmentation from Highly Noisy Microscopic Videos}},
author = {Bowler, John and Feris, Rogério Schmidt and Cao, Liangliang and Wang, Jun and Zhou, Mo},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2015},
pages = {915-920},
doi = {10.1109/WACV.2015.126},
url = {https://mlanthology.org/wacv/2015/bowler2015wacv-automated/}
}