2014/04/16 by Ayushi Sinha, Sinha, Ayushi, William R. Gray Roncal +21
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Electron Microscopy Techniques and Applications #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #cs.CV
paper · pdf · doi:10.48550/arxiv.1404.4800
2 pages, 1 figure
arxiv created 2014/04/16 · openalex publication_date 2014/04/16 · arxiv updated 2014/04/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we present a new pipeline which automatically identifies and annotates axoplasmic reticula, which are small subcellular structures present only in axons. We run our algorithm on the Kasthuri11 dataset, which was color corrected using gradient-domain techniques to adjust contrast. We use a bilateral filter to smooth out the noise in this data while preserving edges, which highlights axoplasmic reticula. These axoplasmic reticula are then annotated using a morphological region growing algorithm. Additionally, we perform Laplacian sharpening on the bilaterally filtered data to enhance edges, and repeat the morphological region growing algorithm to annotate more axoplasmic reticula. We track our annotations through the slices to improve precision, and to create long objects to aid in segment merging. This method annotates axoplasmic reticula with high precision. Our algorithm can easily be adapted to annotate axoplasmic reticula in different sets of brain data by changing a few thresholds. The contribution of this work is the introduction of a straightforward and robust pipeline which annotates axoplasmic reticula with high precision, contributing towards advancements in automatic feature annotations in neural EM data.