PyTorch Connectomics

A semantic and instance segmentation toolbox for EM connectomics.

The field of connectomics aims to reconstruct the wiring diagram of the brain by mapping the neural connections at the level of individual synapses. Recent advances in electronic microscopy (EM) have enabled the collection of a large number of image stacks at nanometer resolution, but the annotation requires expertise and is super time-consuming. Here we provide a deep learning framework powered by PyTorch for automatic and semi-automatic image segmentation in connectomics.

Website: https://connectomics.readthedocs.io/en/latest/
Source code: https://github.com/zudi-lin/pytorch_connectomics

MitoEM Dataset

MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation

Electron microscopy (EM) allows identifying intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. We introduce the MitoEM dataset, a 3D mitochondria instance segmentation dataset with two (30 µm)^3 volumes from human and rat cortices, respectively, 3, 600× larger than previous benchmarks. With around 40K instances, we find a great diversity of mitochondria in shape and density. On MitoEM, we find existing instance segmentation methods often fail to correctly segment mitochondria with complex shapes or close contacts with other instances.

Website: https://mitoem.grand-challenge.org/
Source code: https://connectomics.readthedocs.io/en/latest/tutorials/mito.html

AxonEM Dataset

AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions

Electron microscopy (EM) enables the reconstruction of neural circuits at the level of individual synapses, which has been transformative for scientific discoveries. we introduce the AxonEM dataset, which consists of two 30×30×30 µm^3 EM image volumes from the human and mouse cortex, respectively. We thoroughly proofread over 18,000 axon instances to provide dense 3D axon instance segmentation, enabling largescale evaluation of axon reconstruction methods. In addition, we densely annotate nine ground truth subvolumes for training, per each data volume.

Website: https://connectomics-bazaar.github.io/proj/AxonEM/index.html
Source code: https://github.com/donglaiw/AxonEM-challenge

NucMM Dataset

NucMM Dataset: Neuronal Nuclei Segmentation at Sub-Cubic Millimeter Scale

Segmenting 3D cell nuclei from microscopy image volumes is critical for biological and clinical analysis, enabling the study of cellular expression patterns and cell lineages. We pushed the task forward to the sub-cubic millimeter scale and curated the NucMM dataset with two fully annotated volumes: one 0.1 mm^3 electron microscopy (EM) volume containing nearly the entire zebrafish brain with around 170,000 nuclei; and one 0.25 mm^3 micro-CT (uCT) volume containing part of a mouse visual cortex with about 7,000 nuclei.

Website: https://nucmm.grand-challenge.org/
Source code: https://github.com/zudi-lin/pytorch_connectomics/tree/master/configs/NucMM

Peax

Interactive visual pattern search in sequential data using unsupervised deep representation learning

Peax is a novel feature-based technique for interactive visual pattern search in sequential data based on a convolutional autoencoder for unsupervised representation learning of regions in sequential data. Peax enables interactive feedback-driven adjustments of the pattern search to adapt to the users' perceived similarity, for which an active learning strategy is employed to focus the labeling process on useful regions for training a classifier. ScreenIt has been developed in collaboration with Novartis Institute for Biomedical Research.

Website: http://peax.lekschas.de
Source code: https://github.com/novartis/peax

ICON

An Interactive Approach to Train Deep Neural Networks for Segmentation of Neuronal Structures

A web-based interactive tool for training deep neural networks for segmentation tasks. It consists of a server backend that runs on a high-performance GPU compute node, and a front end user interface that runs on a web browser. User configures a classifier, the classes of objects to be detected, and the set of images to use for training and validation.

Source code: https://github.com/Rhoana/icon

RhoANA

Dense Automatic Neural Annotation

RhoANA is software for dense automatic annotation of neurons in EM serial sections. It includes a processing pipeline, as well as Mojo, a proofreading and annotation tool.

Source code: https://github.com/Rhoana

Skin Reflectance Database

The MERL / ETH Skin Reflectance Database is a set of statistics for parameters of the Torrance-Sparrow and Blinn-Phong analytic BRDF models and face albedo. We derived these statistics from measuring skin reflectance of 149 subjects of varying age, gender, and race. We are making the collected statistics publicly available to the research community for applications in face synthesis and analysis.

Website: https://vcglab.org/facescanning/