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In this paper, we present a new pipeline which automatically identifies andannotates axoplasmic reticula, which are small subcellular structures presentonly in axons. We run our algorithm on the Kasthuri11 dataset, which was colorcorrected using gradient-domain techniques to adjust contrast. We use abilateral filter to smooth out the noise in this data while preserving edges,which highlights axoplasmic reticula. These axoplasmic reticula are thenannotated using a morphological region growing algorithm. Additionally, weperform Laplacian sharpening on the bilaterally filtered data to enhance edges,and repeat the morphological region growing algorithm to annotate moreaxoplasmic reticula. We track our annotations through the slices to improveprecision, and to create long objects to aid in segment merging. This methodannotates axoplasmic reticula with high precision. Our algorithm can easily beadapted to annotate axoplasmic reticula in different sets of brain data bychanging a few thresholds. The contribution of this work is the introduction ofa straightforward and robust pipeline which annotates axoplasmic reticula withhigh precision, contributing towards advancements in automatic featureannotations in neural EM data.

Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes