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The most established method of reconstructing neural circuits from animalsinvolves slicing tissue very thin, then taking mosaics of electron microscope(EM) images. To trace neurons across different images and through differentsections, these images must be accurately aligned, both with the others in thesame section and to the sections above and below. Unfortunately, sectioning andimaging are not ideal processes - some of the problems that make alignmentdifficult include lens distortion, tissue shrinkage during imaging, tears andfolds in the sectioned tissue, and dust and other artifacts. In addition thedata sets are large (hundreds of thousands of images) and each image must bealigned with many neighbors, so the process must be automated and reliable.This paper discusses methods of dealing with these problems, with numericresults describing the accuracy of the resulting alignments.

Automated Alignment of Imperfect EM Images for Neural Reconstruction