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empanada v1.1 now released

empanada and MitoNet

Empanada (EM Panoptic Any Dimension Annotation) is a tool for panoptic segmentation of organelles in electron microscopy (EM) images in 2D and 3D. Panoptic segmentation combines both instance and semantic segmentation enabling a holistic approach to image annotation with a single deep-learning model. To make model training and inference lightweight and broadly applicable to many EM imaging modalities, Empanada runs all expensive operations on 2D images or run-length encoded versions of 3D volumes.

Direct LinkManuscript

Sample preparation widget

The Sample Preparation Widget (SPW) is an online tool available through the EMPIAR deposition system that is designed to allow easy confirmation of protocols with point-and-click functionality. It uses controlled vocabularies where possible and allows free-text information if required. This tool will be used into the future to show what sample preparation protocols were used to create a vEM dataset that has been deposited to EMPIAR.

Direct Link

CEM1.5M

A heterogeneous, non-redundant, information-rich, and relevant unlabeled EM image dataset (at ∼1.5 x 106 images, the largest of its kind to our knowledge) for use as a database to pre-train and sample images for mitochondria, or other organelles, segmentation. We used CEM1.5M to pre-train MitoNet

Dataset uploaded at:

Dataset

CEM-MitoLab

A dataset of ~22K cellular EM 2D images with label maps of ~135K mitochondrial instances, for deep learning. We used this labeled dataset to train MitoNet.

Dataset uploaded at:

Dataset

MitoNet Benchmark

7 benchmark datasets with instance labeled mitochondria, used to test MitoNet. 1 dataset is a series or random TEM images; the others are isotropic CvEM (FIB-SEM) data from various biosamples and sample preparation techniques.

Dataset uploaded at:

Dataset

CEM500K

A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learning methods.

Dataset uploaded at:

Dataset

Full Code Uploaded:

Code

Manuscript on BioRxiv:

BioRxiv