Reconstruction of neural circuits at the microscopic scale of individual neurons and synapses, also known as connectomics, is an important challenge for neuroscience. While an important motivation of connectomics is providing anatomical ground truth for neural circuit models, the ability to decipher neural wiring maps at the individual cell level is also important in studies of many neurodegenerative diseases. Reconstruction of a neural circuit at the individual neuron level requires the use of electron microscopy images due to their extremely high resolution. Computational challenges include pixel-by-pixel annotation of these images into classes such as cell membrane, mitochondria and synaptic vesicles and the segmentation of individual neurons. State-of-the-art image analysis solutions are still far from the accuracy and robustness of human vision and biologists are still limited to studying small neural circuits using mostly manual analysis. In this chapter, we describe our image analysis pipeline that makes use of novel supervised machine learning techniques to tackle this problem.
Revised: September 26, 2016 |
Published: December 1, 2014
Citation
Tasdizen T., M. Seyedhosseini, T. Liu, C. Jones, and E.R. Jurrus. 2014.Image Segmentation for Connectomics Using Machine Learning. In Computational Intelligence in Biomedical Imaging, edited by K Suzuki. 237-278. New York, New York:Springer.PNNL-SA-105561.doi:10.1007/978-1-4614-7245-2_10