https://github.com/arash-keshavarz/ct-recon-seg-

Deep learning pipeline for CT image reconstruction from low-dose scans using U-Net, followed by lung/lesion segmentation. Includes evaluation with PSNR, SSIM, and Dice metrics. Demonstrates potential for enhancing medical imaging quality.

https://github.com/arash-keshavarz/ct-recon-seg-

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (1.1%) to scientific vocabulary
Last synced: 9 months ago · JSON representation

Repository

Deep learning pipeline for CT image reconstruction from low-dose scans using U-Net, followed by lung/lesion segmentation. Includes evaluation with PSNR, SSIM, and Dice metrics. Demonstrates potential for enhancing medical imaging quality.

Basic Info
  • Host: GitHub
  • Owner: Arash-Keshavarz
  • Default Branch: main
  • Size: 1000 Bytes
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created 12 months ago · Last pushed 12 months ago
Metadata Files
Readme

README.md

CT-Recon-Seg-

Deep learning pipeline for CT image reconstruction from low-dose scans using U-Net, followed by lung/lesion segmentation. Includes evaluation with PSNR, SSIM, and Dice metrics. Demonstrates potential for enhancing medical imaging quality.

Owner

  • Name: Arash
  • Login: Arash-Keshavarz
  • Kind: user

GitHub Events

Total
  • Push event: 1
Last Year
  • Push event: 1

Dependencies

requirements.txt pypi
  • albumentations *
  • matplotlib *
  • monai *
  • numpy *
  • opencv-python *
  • pillow *
  • pydicom *
  • scikit-image *
  • scikit-learn *
  • seaborn *
  • simpleitk *
  • streamlit *
  • tensorboard *
  • torch >=1.12
  • torchvision *
  • tqdm *