Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes using High-Resolution Neural EM Data
This work addresses the challenge of connectome reconstruction at an ultrastructure level, which is incremental as it focuses on annotating specific subcellular structures to aid segmentation.
The paper tackles the problem of accurately tracking neural processes across many image slices for connectome estimation by proposing a novel method to automatically identify and annotate axoplasmic reticula in high-resolution neural EM data, resulting in high precision annotations that can improve automatic segmentation.
Accurately estimating the wiring diagram of a brain, known as a connectome, at an ultrastructure level is an open research problem. Specifically, precisely tracking neural processes is difficult, especially across many image slices. Here, we propose a novel method to automatically identify and annotate small subcellular structures present in axons, known as axoplasmic reticula, through a 3D volume of high-resolution neural electron microscopy data. Our method produces high precision annotations, which can help improve automatic segmentation by using our results as seeds for segmentation, and as cues to aid segment merging.