Under copyright Constraint(s) on Use: This work is protected by the U.S. Copyright Law (Title 17, U.S.C.). Use of this work beyond that allowed by "fair use" or any license applied to this work requires written permission of the copyright holder(s). Responsibility for obtaining permissions and any use and distribution of this work rests exclusively with the user and not the UC San Diego Library. Inquiries can be made to the UC San Diego Library program having custody of the work. Use: This work is available from the UC San Diego Library. This digital copy of the work is intended to support research, teaching, and private study.
Advances in volume electron microscopy imaging have enabled the accumulation of large-scale, high-resolution biological data. In the neuroscience domain, the potential for these volumes to characterize the dense network of intertwining structures of the brain along with its subcellular components is within reach, but still limited in many ways by the size and complexity of the analysis. We have utilized the high-speed storage and GPU resources provided by the Pacific Research Platform (PRP) and CHASE-CI, managed by the Kubernetes engine, Nautilus, to implement and validate a 3D U-Net for multi-class volumetric segmentation trained on a scarcely labeled mouse brain dataset. A deep learning internal zero-shot superresolution was evaluated on downsampled volumes to simulate its effect on pixel classification after x-y resolution boosting. In addition to traditional model performance metrics, a volume rendering tool was created to visualize voxel-level predictions to better understand problematic structures and subcellular features. These models and tools extend the neuroimaging reconstruction framework, NeuroKube. Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp) Banuprakash, Shyam; Hein, Ellen; Nguyen, Kim; Wahl, Justin; Yu, Yuping; Madany, Matthew; Ellisman, Mark (2021). Volumetric Segmentation in Electron Microscopy Brain Imaging. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0ZP461G
Type
dataset
Identifier
ark:/20775/bb09979489
Language
English
Subject
Capstone projects Volumetric segmentation Neuroscience Alzheimer's disease Convolutional Neural Network (CNN) U-NET Data Science & Engineering Master of Advanced Study (DSE MAS) Zero-shot superresolution (ZSSR) Volume electron microscopy Nautilus DSE MAS - 2021 Cohort
If you're wondering about permissions and what you can do with this item, a good starting point is the "rights information" on this page. See our terms of use for more tips.
Share your story
Has Calisphere helped you advance your research, complete a project, or find something meaningful? We'd love to hear about it; please send us a message.
