https://github.com/radxtools/radpathfusion
Jupyter notebook with Python-based workflow for co-registration of radiographic imaging (MRI/CT etc.) with digitized pathology images, and mapping annotations from pathology onto imaging.
https://github.com/radxtools/radpathfusion
cancer-imaging-research computational-imaging digital-pathology docker image-registration itcr python radiology radiomics
Last synced: 3 months ago
JSON representation
Jupyter notebook with Python-based workflow for co-registration of radiographic imaging (MRI/CT etc.) with digitized pathology images, and mapping annotations from pathology onto imaging.
- Host: GitHub
- URL: https://github.com/radxtools/radpathfusion
- Owner: radxtools
- Created: 2020-09-29T16:53:24.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-10-05T01:00:24.000Z (over 2 years ago)
- Last Synced: 2023-10-20T11:43:06.502Z (over 1 year ago)
- Topics: cancer-imaging-research, computational-imaging, digital-pathology, docker, image-registration, itcr, python, radiology, radiomics
- Language: Jupyter Notebook
- Homepage:
- Size: 99.3 MB
- Stars: 3
- Watchers: 6
- Forks: 1
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Overview
This notebook provides an interactive workflow to load in a radiographic imaging volume (MRI, CT, etc.) with a corresponding digitized pathology image, and go through co-registration to map one onto the other. Functionalities include selecting corresponding sections, basic image manipulation and scaling as pre-processing steps, as well as selecting corresponding landmarks to run a deformable co-registration between the modalities. The deformation can then be applied to any annotation to map it from one to the other (e.g. map cancer annotation from pathology image onto MR image) within the noteboo. Results are visualized and fully editable and updatable within the notebook workflow.
# References
If you make use of this implementation, please cite the following paper:
Antunes, JT, Viswanath, SE, Brady, JT, Crawshaw, B, Ros, P, Steele, S, Delaney, CP, Paspulati, RM, Willis, JE, Madabhushi, A, "Coregistration of Preoperative MRI with Ex Vivo Mesorectal Pathology Specimens to Spatially Map Post-treatment Changes in Rectal Cancer Onto In Vivo Imaging: Preliminary Findings", Acad Radiol, 2018 Jul;25(7):833-841.
## Prerequisites
The below software and configurations will be needed to execute the notebooks:
* Docker
* Update docker with 8 gigs of memory
* git
# Getting started with Rad Path Fusion
## Running with docker
First clone this repository
```
git clone https://github.com/Theta-Tech-AI/radpathfusion.git
```
Second, we will quickly configure the docker memory
## Docker Setup:
1. Open Docker Destop
2. Open settings
2. Click on resources as seen on the below screen shot
4. Move the memory limit to 8 GB
Lastly, we will run the docker image using either:
1. docker-compose
2. docker run
### docker-compose
Run the following commands to start the docker container
```
cd docker
docker-compose up
```
### docker run
With this step, you don't need to clone the url.
Run the following commands to start the docker container
```
docker rm radxtools/radpathfusion-examples
docker pull radxtools/radpathfusion-examples
docker run -d -p 3000:3000 --name radpathfusion-examples radxtools/radpathfusion-examples
```
## Tutorials
Once the docker image is up and running. You can view our notebooks. You can get started with the notebook to learn how to use the package. You should start with `notebooks/Rad Path Fusion Final.ipynb`
The notebooks can be viewed by opening the browser and visting the url http://localhost:3000
A walk through of the notebook with screen shots can be found [here](/docs/notebook_starter.md)
# Contribution Guide:
Please follow google style formatting for [docstrings](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings)
## Bugs and Feature Request
Please submit bugs and features to our github page.
## Pull Requests
Create a issue on our board.
Create a pull request with your changes. Tag your changes with the issue number (commit message should have issue number).
Someone from the team will review your request and merge your changes for the next release.