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mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops.

Elisa C Pavarino1, Emma Yang2, Nagaraju Dhanyasi2

  • 1Department of Neurobiology, Harvard Medical School, Boston, MA, USA.

Biorxiv : the Preprint Server for Biology
|May 3, 2023
PubMed
Summary
This summary is machine-generated.

We developed mEMbrain, a user-friendly MATLAB software for segmenting electron microscopy datasets, accelerating connectomics research. This tool simplifies neural reconstruction, making advanced analyses accessible without coding, and includes valuable annotated datasets and pre-trained networks.

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Image Analysis

Background:

  • Connectomics relies on reconstructing neural circuits from electron microscopy (EM) data.
  • Automated segmentation methods have advanced, but user-friendly tools for neuroscience are needed.
  • Existing tools often require coding expertise, limiting accessibility for many researchers.

Approach:

  • Developed mEMbrain, an interactive MATLAB-based software for EM dataset segmentation.
  • Integrated mEMbrain with the VAST tool for ground truth generation, preprocessing, and deep learning.
  • Provided a user-friendly interface for labeling, segmentation, and analysis on Linux and Windows.

Key Points:

  • mEMbrain expedites manual labeling and offers semi-automatic instance segmentation for MATLAB users.
  • The software was validated on diverse datasets across species, scales, and developmental stages.
  • Released an extensive EM ground truth resource (180+ hours expert annotation) and 4 pre-trained networks.

Conclusions:

  • mEMbrain offers a no-code solution for lab-based neural reconstructions, lowering the barrier to connectomics.
  • The tool aims to democratize advanced neuroimaging analysis within the neuroscience community.
  • Availability of software, datasets, and networks facilitates affordable and efficient connectomics research.