The code for the implementation of the innovative image registration method that we detailed in our paper "Advanced Feature Extraction and Outlier Detection in 3D Biological/Biomedical Image Registration" is available in this repository. Our approach exhibits superior performance compared to other contemporary methods in a range of scenarios and demonstrates dominance across multiple image quality metrics.
By addressing the difficulties presented by distortions and transformations, our method for image registration demonstrates efficacy in ensuring the reliable and high-quality registration of images. The approach has demonstrated remarkable efficacy when applied to intricate 3D multiplex microscopy and 3D MRI images.
To install and run this project, you will need Python 3.6 or later. Clone the repository and install the necessary requirements:
git clone https://github.com/NabaviLab/Code
cd 3D Biological:Biomedical Image Registration.ipynb
And to. install libraries, please follow the below command in the first cell of the Google Colab/Jupyter Notebook:
!pip install opencv-python-headless numpy torch torchvision pillow scipy pandas matplotlib seaborn plotly scikit-image psutil scikit-learn
This repository contains three .ipynb
(Jupyter Notebook) files:
ResNet 50 Fine Tuning.ipynb
: This file allows you to fine-tune the ResNet-50 model based on your dataset.3D Biological:Biomedical Image Registration.ipynb
: This is the main registration file where the registration algorithm is implemented.Evaluation_Metrics.ipynb
: The evaluation metrics that we used in this paper are presented in this script.
You can use any 3D biological/biomedical images for registration. However, our model performs best with 3D multiplex microscopy and 3D MRI images. For your convenience, we provide the "CUMC12" dataset, which is synthesised based on known transformations. This dataset is ideal for testing the algorithm. The "CUMC12" dataset we used in our paper is publicly available:
https://continuousregistration.grand-challenge.org/data/
In case you use the "CUMC12" dataset, please follow the citation guidelines below:
@article{klein2009evaluation,
title={Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration},
author={Klein, Arno and Andersson, Jesper and Ardekani, Babak A and Ashburner, John and Avants, Brian and Chiang, Ming-Chang and Christensen, Gary E and Collins, D Louis and Gee, James and Hellier, Pierre and others},