Paper Info
Title
Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes.
Abstract
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.
Year
Venue
Field
2014
arXiv: Computer Vision and Pattern Recognition
Sharpening,Computer vision,Annotation,Pattern recognition,Region growing algorithm,Computer science,Connectome,Artificial intelligence,Merge (version control),Bilateral filter
DocType
Volume
Citations 
Journal
abs/1404.4800
0
PageRank 
References 
Authors
0.34
0
11
Name
Order
Citations
PageRank
Ayushi Sinha1246.72
William Gray Roncal2388.25
Narayanan Kasthuri3627.11
Ming Chuang41626.77
Priya Manavalan5212.63
Dean Kleissas6142.71
Joshua T. Vogelstein727331.99
R. J. Vogelstein826924.60
Randal Burns91955115.15
Jeff W. Lichtman1013412.41
Michael M. Kazhdan1147622.36