Open data
|
Deployed on AWS
The Automated Segmentation of intracellular substructures in Electron Microscopy (ASEM) project provides deep learning models trained to segment structures in 3D images of cells acquired by Focused Ion Beam Scanning Electron Microscopy (FIB-SEM). Each model is trained to detect a single type of structure (mitochondria, endoplasmic reticulum, golgi apparatus, nuclear pores, clathrin-coated pits) in cells prepared via chemically-fixation (CF) or high-pressure freezing and freeze substitution (HPFS). You can use our open source pipeline to load a model and predict a class of sub-cellular structures in naive FIB-SEM cells images. If required, a fine-tuning procedure allows a model to be trained on a small amount of additional ground truth annotations to improve a prediction on a naive dataset. Together with the trained models, we also provide the training, validation and test datasets.
Overview
The Automated Segmentation of intracellular substructures in Electron Microscopy (ASEM) project provides deep learning models trained to segment structures in 3D images of cells acquired by Focused Ion Beam Scanning Electron Microscopy (FIB-SEM). Each model is trained to detect a single type of structure (mitochondria, endoplasmic reticulum, golgi apparatus, nuclear pores, clathrin-coated pits) in cells prepared via chemically-fixation (CF) or high-pressure freezing and freeze substitution (HPFS). You can use our open source pipeline to load a model and predict a class of sub-cellular structures in naive FIB-SEM cells images. If required, a fine-tuning procedure allows a model to be trained on a small amount of additional ground truth annotations to improve a prediction on a naive dataset. Together with the trained models, we also provide the training, validation and test datasets.
Features and programs
Open Data Sponsorship Program
This dataset is part of the Open Data Sponsorship Program, an AWS program that covers the cost of storage for publicly available high-value cloud-optimized datasets.
Pricing
This is a publicly available data set. No subscription is required.
Legal
Content disclaimer
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Delivery details
AWS Data Exchange (ADX)
AWS Data Exchange is a service that helps AWS easily share and manage data entitlements from other organizations at scale.
Open data resources
Available with or without an AWS account.
- How to use
- To access these resources, reference the Amazon Resource Name (ARN) using the AWS Command Line Interface (CLI). Learn more
- Description
- High resolution 3D cell image datasets
- Resource type
- S3 bucket
- Amazon Resource Name (ARN)
- arn:aws:s3:::asem-project/datasets/
- AWS region
- us-east-1
- AWS CLI access (No AWS account required)
- aws s3 ls --no-sign-request s3://asem-project/datasets//
- Description
- Trained ML segmentation models for use in ASEM pipeline
- Resource type
- S3 bucket
- Amazon Resource Name (ARN)
- arn:aws:s3:::asem-project/models/
- AWS region
- us-east-1
- AWS CLI access (No AWS account required)
- aws s3 ls --no-sign-request s3://asem-project/models//
Resources
Vendor resources
Support
Contact
Managed By
Kirchhausen Lab at Harvard Medical School
How to cite
Automated Segmentation of Intracellular Substructures in Electron Microscopy (ASEM) on AWS was accessed on DATE from https://registry.opendata.aws/asem-project .
License
All available datasets and models are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License
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