Paper Info
Title
Transfer Learning Analysis Of Image Processing Workflows For Electron Microscopy Datasets
Abstract
Neuroscientists are collecting Electron Microscopy (EM) datasets at increasingly faster rates. This modality offers an unprecedented map of brain structure at the resolution of individual neurons and their synaptic connections. Despite sophisticated image processing algorithms such as Flood Filling Networks, these huge datasets often require large amounts of hand-labeled data for algorithm training, followed by significant human proofreading. Many of these challenges are common across neuroscience modalities (and in other domains), but we use EM as a use case because the scale of this data emphasizes the opportunity and impact of rapidly transferring methods to new datasets. We investigate transfer learning for these workflows, exploring transfer to different regions within a dataset, between datasets from different species, and for datasets collected with different image acquisition techniques. For EM data, we investigate the impact of algorithm performance at different workflow stages. Finally, we assess the impact of candidate transfer learning strategies in environments with no training labels. This work provides a library of algorithms, pipelines, and baselines on established datasets. We enable rapid assessment and improvements to processing pipelines, and an opportunity to quickly and effectively analyze new datasets for the neuroscience community.
Year
DOI
Venue
2019
10.1109/IEEECONF44664.2019.9048673
CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS
Keywords
DocType
ISSN
Electron Microscopy, Image Segmentation, Transfer Learning, Unsupervised Learning
Conference
1058-6393
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Erik C. Johnson101.35
Luis M. Rodriguez200.34
Raphael Norman-Tenazas300.34
Daniel Xenes400.68
William Gray Roncal5388.25