40 citations · 182 across the 16 of their papers we have counts for
10 papers · 1 filter
NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale
Zudi Lin, Donglai Wei, Mariela D. Petkova +12
Segmenting 3D cell nuclei from microscopy image volumes is critical for biological and clinical analysis, enabling the study of cellular expression patterns and cell lineages. Howe…
VICE: Visual Identification and Correction of Neural Circuit Errors
Felix Gonda, Xueying Wang, Johanna Beyer +3
A connectivity graph of neurons at the resolution of single synapses provides scientists with a tool for understanding the nervous system in health and disease. Recent advances in…
A Topological Nomenclature for 3D Shape Analysis in Connectomics
Abhimanyu Talwar, Zudi Lin, Donglai Wei +7
One of the essential tasks in connectomics is the morphology analysis of neurons and organelles like mitochondria to shed light on their biological properties. However, these biolo…
Detecting Synapse Location and Connectivity by Signed Proximity Estimation and Pruning with Deep Nets
Toufiq Parag, Daniel Berger, Lee Kamentsky +5
Synaptic connectivity detection is a critical task for neural reconstruction from Electron Microscopy (EM) data. Most of the existing algorithms for synapse detection do not identi…
Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration
Toufiq Parag, Fabian Tschopp, William Grisaitis +6
The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals. Most of these neural reconstructions were computed…
Morphological Error Detection in 3D Segmentations
David Rolnick, Yaron Meirovitch, Toufiq Parag +5
Deep learning algorithms for connectomics rely upon localized classification, rather than overall morphology. This leads to a high incidence of erroneously merged objects. Humans,…
Guided Proofreading of Automatic Segmentations for Connectomics
Daniel Haehn, Verena Kaynig, James Tompkin +2
Automatic cell image segmentation methods in connectomics produce merge and split errors, which require correction through proofreading. Previous research has identified the visual…
Icon: An Interactive Approach to Train Deep Neural Networks for Segmentation of Neuronal Structures
Felix Gonda, Verena Kaynig, Ray Thouis +4
We present an interactive approach to train a deep neural network pixel classifier for the segmentation of neuronal structures. An interactive training scheme reduces the extremely…
Automatic Annotation of Axoplasmic Reticula in Pursuit of Connectomes using High-Resolution Neural EM Data
Ayushi Sinha, William Gray Roncal, Narayanan Kasthuri +3
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 processe…
VESICLE: Volumetric Evaluation of Synaptic Interfaces using Computer vision at Large Scale
William Gray Roncal, Michael Pekala, Verena Kaynig-Fittkau +7
An open challenge problem at the forefront of modern neuroscience is to obtain a comprehensive mapping of the neural pathways that underlie human brain function; an enhanced unders…