An optimal-path approach for neural circuit reconstruction

Abstract

Neurobiologists are collecting large amounts of electron microscopy image data to gain a better understanding of neuron organization in the central nervous system. Image analysis plays an important role in extracting the connectivity present in these images; however, due to the large size of these datasets, manual analysis is essentially impractical. Automated analysis, however, is challenging because of the difficulty in reliably segmenting individual neurons in 3D. In this paper, we describe an automatic method for finding neurons in sequences of 2D sections. The proposed method formulates the problem of finding paths through sets of sections as an optimal path computation, which applies a cost function to the identification of a cell from one section to the next and solves this optimization problem using Dijkstra's algorithm. This basic formulation allows us to account for variability or inconsistencies between sections and to prioritize cells based on the evidence of their connectivity.

Automation of 3D reconstruction of neural tissue from large volume of conventional serial section transmission electron micrographs

“…High performance and substantial savings in reconstruction time had been reported (Macke et al, 2008). At the same time little effort had been devoted to development of such tools for conventional ssTEM even though substantially larger amount of data is available with higher resolution and richer ultra-structural contents (see though (Jurrus et al, 2008)). …”

Robust Segmentation Based Tracing Using an Adaptive Wrapper for Inducing Priors

“…There exists substantial prior work in segmentation based tracing, with frameworks ranging from shortest path based [2], watersheds [3], [4], random walkers [5], active contours based snakes [6], geodesic active contours, vector flows [7], active contours without edges [8], and discrete valued Markov Random Fields (MRFs) inferred using graph cuts [9], [10]. The aim of this paper is to present a method that is capable of wrapping around a non-parametric segmentation technique, thereby achieving two objectives: Firstly, the proposed technique is capable of embedding high level priors into the tracing algorithm using free parameters of the base segmenter.…”

Hidden Markov models for tracking neuronal structure contours in electron micrograph stacks

“…However, the target objects do not split or merge, and the deformation and translation in their data set is relatively small. In [8], segmentation is performed on each frame to acquire all cells (or neurons), which are then matched over frames to achieve 3-D reconstruction. However, to the best of our knowledge, no single segmentation method works well on highly cluttered data as we have had to segment every cell.…”