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batch-processing-multimodality-tumour-imaging

This code is designed to help simplify the processing (co-registration, 2D and 3D (semi-automatic) segmentation, and quantification) of longitudinally collected 2D and 3D imaging data. The code was originally intended for processing of Provided 2D

https://github.com/nallam1/batch-processing-multimodality-tumour-imaging

Science Score: 44.0%

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    Low similarity (5.3%) to scientific vocabulary
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Repository

This code is designed to help simplify the processing (co-registration, 2D and 3D (semi-automatic) segmentation, and quantification) of longitudinally collected 2D and 3D imaging data. The code was originally intended for processing of Provided 2D

Basic Info
  • Host: GitHub
  • Owner: nallam1
  • License: mit
  • Language: MATLAB
  • Default Branch: main
  • Size: 18.8 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 4 years ago · Last pushed almost 3 years ago
Metadata Files
Readme License Citation

README.md

Batch-processing-multimodality-tumour-imaging

This code is designed to help simplify the batch processing (co-registration, 2D and 3D (semi-automatic) segmentation, and quantification) of longitudinally collected 2D and 3D multi-modality imaging data using Matlab. The code was originally intended for processing of tumour response kinetics following irradiation pre-clinically in a window chamber model, data acquired using optical imaging modalities (brightfield, epifluorescence, and optical coherence tomography angiography). Provided for example 4D (BM mode acquired OCT data (with ideally ~5 repetitions of B-scans per y-step of the scanning-mirror galvonometer) and 2D en-face view microscopy images, the code aims to perform the following tasks:

1) 2D lateral mask creation and 2D co-registration of OCT to brightfield/fluorescence images; 2) Quantification of brightfield and fluorescence images for automatic extraction of tumour viability and approximate volume; 3) 3D co-registration of OCT scans inter-timepoints; 4) 3D tumour mask creation; 5) 3D Vessel segmentation (manual or in the future automatically); 6) Quantification of vascular morphology from segmentation within defined VOI.

*Disclaimer: My background is in biophysics and medical biophysics; the code may more than definitely be far from having optimal performance.

Owner

  • Name: Nader Allam
  • Login: nallam1
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below. Thank you"
authors:
- family-names: "Allam"
  given-names: "Nader"
  orcid: "https://orcid.org/0000-0003-3338-0815"
title: "Batch-processing-multimodality-tumour-imaging"
version: 1.0.0
date-released: 2022-03-08
url: "https://github.com/nallam1/Batch-processing-multimodality-tumour-imaging"

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