SNEMI3D: 3D Segmentation of neurites in EM images

Submitted by Perrine on Mon, 03/06/2023 - 16:45

In this challenge, a full stack of electron microscopy (EM) slices will be used to train machine-learning algorithms for the purpose of automatic segmentation of neurites in 3D. This imaging technique visualizes the resulting volumes in a highly anisotropic way, i.e., the x- and y-directions have a high resolution, whereas the z-direction has a low resolution, primarily dependent on the precision of serial cutting.


BioImage.IO -- a collaborative effort to bring AI models to the bioimaging community. 

  • Integrated with Fiji, ilastik, ImJoy and more
  • Try model instantly with BioEngine
  • Contribute your models via Github

This is a database of pretrained deep Learning models. 

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Rivuletpy dockerised workflow for BIAFLOWS.

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Vaa3d All-Path-Pruning 2.0 (APP2) dockerised workflow for BIAFLOWS.

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Web based viewer developped for google for very big data: 

Neuroglancer is a WebGL-based viewer for volumetric data. It is capable of displaying arbitrary (non axis-aligned) cross-sectional views of volumetric data, as well as 3-D meshes and line-segment based models (skeletons). The segmentation has to be done before loading the dataset, it is not done Inside the viewer.

This is not an official Google product.

It has among other the nice feature of beeing able to generate url for sharing a specific view.

Note that the only supported browser for now are 

  • Chrome >= 51
  • Firefox >= 46