DICOM 3D Medical Image Modeling (DMIM)

Nowadays, patients are sent to MRI, PET, and CT scans more than before. Each scan produces a large amount of information of a patient, normally as a set of 2D slices, that will be inspected by a doctor or a technician. The project aims to visualize any DICOM images by creating a 3D model in addition to the classic slice-by-slice inspection.

Demo: https://www.youtube.com/watch?v=Xz3xwpmoMHg

 

Installation

This tutorial uses Conda and PIP. Please make sure to install them before you proceed the next steps.

1. Create virtual environment with the following instruction:

$ conda create -n DMIM python=3.5.4

“DMIM” is the name of the new environment. Please make sure to install Python 3.5.4, and not other version.

2. Activate the new DMIM environment by typing:

$ conda activate DMIM

3. Clone the repository:

$ git clone https://github.com/nis1/Visualization-DMIM.git

4. Install MongoDB:

$ conda install mongodb

5. Enter the repository folder, and install the requirements:

$ pip install -r requirements.txt

6. Open new terminal window and start MongoDB:

$ mongod

7. Finally, run the website from the repository main folder:

$ FLASK_APP=app.py FLASK_DEBUG=1 python -m flask run

8. The website should be available at http://localhost:5000/

A successful installation will result in the following index page:

  

More images from the app

Store and analyze various cases

 

3D Analysis

 

Slice Analysis

 

Draw on slices

  

References

The X ToolKit: https://github.com/xtk/X

AMI: https://github.com/FNNDSC/ami