Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages.
The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them.
FlyEM Project, HHMI Ashburn, VA, USA.
Juan
J
Nunez-Iglesias
FlyEM Project, HHMI Ashburn, VA, USA ; Department of Computer and Information Science, School of Engineering and Applied Sciences, University of Pennsylvania Philadelphia, PA, USA.
Ryan
R
Kennedy
FlyEM Project, HHMI Ashburn, VA, USA.
Stephen M
SM
Plaza
Video Computing Group, Department of Electrical Engineering, University of California at Riverside Riverside, CA, USA.
Anirban
A
Chakraborty
FlyEM Project, HHMI Ashburn, VA, USA.
William T
WT
Katz
Python; connectomics; electron microscopy; image segmentation; machine learning