TY - GEN
T1 - Automated axon segmentation from highly noisy microscopic videos
AU - Bowler, John
AU - Feris, Rogerio
AU - Cao, Liangliang
AU - Wang, Jun
AU - Zhou, Mo
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/2/19
Y1 - 2015/2/19
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84925423599
U2 - 10.1109/WACV.2015.126
DO - 10.1109/WACV.2015.126
M3 - 会议稿件
AN - SCOPUS:84925423599
T3 - Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015
SP - 915
EP - 920
BT - Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015
Y2 - 5 January 2015 through 9 January 2015
ER -