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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.

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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.

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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:



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:


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:



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

Dataset uploaded at:


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Manuscript on BioRxiv: