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.
Data is added as it becomes available
All available datasets and models are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License
Kirchhausen Lab at Harvard Medical School
See all datasets managed by Kirchhausen Lab at Harvard Medical School.
Automated Segmentation of Intracellular Substructures in Electron Microscopy (ASEM) on AWS was accessed on
DATE from https://registry.opendata.aws/asem-project.
aws s3 ls --no-sign-request s3://asem-project/datasets/
aws s3 ls --no-sign-request s3://asem-project/models/