Candidate Sampling for Neuron Reconstruction from Anisotropic Electron Microscopy Volumes
- 1 January 2014
- book chapter
- conference paper
- Published by Springer Science and Business Media LLC in Lecture Notes in Computer Science
- Vol. 17, 17-24
- https://doi.org/10.1007/978-3-319-10404-1_3
Abstract
The automatic reconstruction of neurons from stacks of electron microscopy sections is an important computer vision problem in neuroscience. Recent advances are based on a two step approach: First, a set of possible 2D neuron candidates is generated for each section independently based on membrane predictions of a local classifier. Second, the candidates of all sections of the stack are fed to a neuron tracker that selects and connects them in 3D to yield a reconstruction. The accuracy of the result is currently limited by the quality of the generated candidates. In this paper, we propose to replace the heuristic set of candidates used in previous methods with samples drawn from a conditional random field (CRF) that is trained to label sections of neural tissue. We show on a stack of Drosophila melanogaster neural tissue that neuron candidates generated with our method produce 30% less reconstruction errors than current candidate generation methods. Two properties of our CRF are crucial for the accuracy and applicability of our method: (1) The CRF models the orientation of membranes to produce more plausible neuron candidates. (2) The interactions in the CRF are restricted to form a bipartite graph, which allows a great sampling speed-up without loss of accuracy.Keywords
This publication has 10 references indexed in Scilit:
- Towards semi-automatic reconstruction of neural circuits.Neuroinformatics, 2012
- Efficient automatic 3D-reconstruction of branching neurons from EM dataPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Segmentation fusion for connectomicsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Detection of neuron membranes in electron microscopy images using a serial neural network architectureMedical Image Analysis, 2010
- Neuron geometry extraction by perceptual grouping in ssTEM imagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- Co-clustering of image segments using convex optimization applied to EM neuronal reconstructionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- Geometrical Consistent 3D Tracing of Neuronal Processes in ssTEM DataLecture Notes in Computer Science, 2010
- Automation of 3D reconstruction of neural tissue from large volume of conventional serial section transmission electron micrographsJournal of Neuroscience Methods, 2009
- An Introduction to Conditional Random Fields for Relational LearningPublished by MIT Press ,2007
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of ImagesIeee Transactions On Pattern Analysis and Machine Intelligence, 1984