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Updated: Jul 24, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

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

Elisa C Pavarino¹, Emma Yang¹, Nagaraju Dhanyasi¹

¹Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, United States.

Frontiers in Neural Circuits

|July 3, 2023

Summary

This summary is machine-generated.

mEMbrain is a user-friendly, open-source MATLAB software for segmenting electron microscopy datasets, accelerating connectomics research. It offers tools for neural reconstructions without requiring coding, making advanced analyses more accessible.

Keywords:

MATLAB VAST affordable connectomics deep learning lightweight software segmentation semi-automatic neural circuit reconstruction volume electron microscopy

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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

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Published on: August 13, 2014

Area of Science:

Neuroscience
Computational Biology
Image Analysis

Background:

Connectomics relies on reconstructing neural circuits from electron microscopy (EM) data.
Automated segmentation methods using deep learning have advanced EM data analysis.
There is a need for accessible, open-source tools for neuroscience image analysis.

Purpose of the Study:

To introduce mEMbrain, an interactive MATLAB-based software for labeling and segmenting EM datasets.
To provide user-friendly tools for advanced analyses in connectomics research.
To expedite manual labeling and offer semi-automatic segmentation for MATLAB users.

Main Methods:

Developed mEMbrain, a MATLAB software with a graphical user interface for Linux and Windows.
Integrated mEMbrain with the VAST tool for ground truth generation, preprocessing, and deep neural network training.
Tested mEMbrain on diverse EM datasets across species, scales, and developmental stages.

Main Results:

mEMbrain facilitates user-friendly labeling and segmentation of EM datasets.
Provided a valuable EM resource with 180 hours of expert annotations across five datasets.
Released four pre-trained networks to support connectomics research.

Conclusions:

mEMbrain offers a no-code solution for lab-based neural reconstructions, enhancing accessibility to connectomics.
The software and provided resources aim to accelerate the pace of connectomics research.
Empowers MATLAB users with semi-automatic segmentation tools for efficient data analysis.