Abstract

Investigations of biological ultrastructure, such as compre- hensive mapping of connections within a nervous system, increasingly rely on large, high-resolution electron microscopy (EM) image volumes. However, discontinuities between the registered section images from which these volumes are assembled, due to variations in imaging con- ditions and section thickness, among other artifacts, impede truly 3- D analysis of these volumes. We propose an optimization procedure, called EMISAC (EM Image Stack Artifact Correction), to correct these discontinuities. EMISAC optimizes the parameters of spatially varying linear transformations of the data in order to minimize the squared norm of the gradient along the section axis, sub ject to detail-preserving regularization. Assessment on a mouse cortex dataset demonstrates the effectiveness of our approach. Relative to the original data, EMISAC produces a large improvement both in NIQE score, a measure of statistical similarity be- tween orthogonal cross-sections and the original image sections, as well as in accuracy of neurite segmentation, a critical task for this type of data. Compared to a recent independently-developed gradient-domain algo- rithm, EMISAC achieves significantly better NIQE image quality scores, and equivalent segmentation accuracy; future segmentation algorithms may be able to take advantage of the higher image quality. In addition, on several time-lapse videos, EMISAC significantly re- duces lighting artifacts, resulting in greatly improved video quality. A software release is available at http://rll.berkeley.edu/2014