Volumetric Segmentation in Electron Microscopy Brain Imaging
Input data
File Size |
|
File Format |
|
Scope And Content | Raw and labeled VEM mouse brain images |
Output data
File Size |
|
File Format |
|
Scope And Content | Output labeled images from binary and multiclass models, and ZSSR |
- Collection
- Cite This Work
-
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
- Description
-
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.
- Creation Date
- 2021-01-09 to 2021-06-04
- Date Issued
- 2021
- Advisors
- Contributors
- Series
- Topics
Formats
View formats within this collection
- Language
- English
- Identifier
- Related Resources
- Github repository for ZSSR source code. https://github.com/HarukiYqM/pytorch-ZSSR
- GitLab repository for voxel classifier source code. https://gitlab.nautilus.optiputer.net/madany/vclass
- M. Madany, K. Marcus, S. Peltier, M. H. Ellisman and I. Altintas, "NeuroKube: An Automated and Autoscaling Neuroimaging Reconstruction Framework using Cloud Native Computing and A.I.," 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 320-330. https://doi.org/10.1109/BigData50022.2020.9378053
- Shocher, Assaf et al. "'Zero-Shot' Super-Resolution using Deep Internal Learning. https://arxiv.org/abs/1712.06087
Software
Reference
- License
-
Creative Commons Attribution 4.0 International Public License
- Rights Holder
- Banuprakash, Shyam; Hein, Ellen; Nguyen, Kim; Wahl, Justin; Yu, Yuping
- Copyright
-
Under copyright (US)
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.
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.
- Digital Object Made Available By
-
Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp)
- Last Modified
2022-11-28