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ShuTu Open-Source Software for Neuron Reconstruction

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Software
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ShuTu Open-Source Software for Neuron Reconstruction

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ShuTu

ShuTu (Chinese for “dendrite”) is a software platform for semi-automated reconstruction of neuronal morphology. It is designed for neurons stained following patch-clamp recording and biocytin filling/staining. It can also handle fluorescence images from confocal stacks, however, by first inverting the images.

Even when there is only a single neuron in the imaged volume, automated reconstruction is tricky because of background. Thus, ShuTu was developed to facilitate a two-step process: sophisticated algorithms perform the automated reconstruction, which is then superimposed on the images, along with a convenient user interface to facilitate efficient completion of the reconstruction via manual annotation.

ShuTu was written by Dezhe Jin (Penn State University) and Ting Zhao (Janelia) in collaboration with Nelson Spruston (Janelia).

A more detailed description of ShuTu can be found here.

Tutorial

Video tutorial

Installation

To install on Mac and Linux, download the zip file, and in a terminal and in the directory ShuTuMac or ShuTuUbuntu, type the commands

  • sudo sh build.sh
  • bash
  • During the process, enter "yes" or "y" or "Y" when prompted. 

    On Windows 10, unzip ShuTuWin64.zip to get the GUI software. To install programs for image processing and automatic tracing, install Ubuntu app in the App Store. Here are the steps:

  • Update Windows 10 to the current version. Ubuntu app requires Windows 10 version 16215 and up.
  • From App Store, install Ubuntu app.
  • Use "Turn Windows features on or off," click on "Windows Subsystem for Linux," click OK, reboot.
  • Start Ubuntu app.
  • sudo apt-get install unzip
  • wget http://personal.psu.edu/dzj2/ShuTu/ShuTuUbuntu.zip
  • unzip ShuTuUbuntu.zip
  • cd ShuTuUbuntu
  • sudo sh build.sh
  • bash

View source code and collaborate on Github.

Images

Download images for practicing reconstruction. 

More Data

Below are some additional images and reconstructions of the neurons described in the paper.

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Contact

Michael Perham
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