superccm 1.0.1

An Open-Source Python Toolkit for Automated Quantification of Corneal Nerve Fibers in Confocal Microscopy Images

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✨ SuperCCM v1.0

🧠 A Fully Open-Source Framework for Corneal Confocal Microscopy (CCM) Image Analysis

English | 简体中文

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🚀 Overview

💡 What is SuperCCM? Why do we need it?

✨ SuperCCM is an open-source Python framework for analyzing corneal nerve images obtained from
Corneal Confocal Microscopy (CCM).
Given a single CCM image, SuperCCM can automatically perform preprocessing, segmentation,
and compute clinically relevant morphological parameters.

It also provides a modular architecture, allowing you to easily integrate your own algorithms
(e.g., segmentation, denoising, or custom workflows).

🧩 Scientific Motivation
Over the past 20 years, CCM-based corneal nerve morphology has proven to be a reliable biomarker
for various neurodegenerative (e.g., diabetic neuropathy, Parkinson’s disease)
and ocular surface disorders (e.g., dry eye).
Existing tools like CCMetrics, NeuronJ/ImageJ, and ACCMetrics are either semi-automatic
or closed-source.
SuperCCM aims to provide a transparent, efficient, and fully open alternative for the community.

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🔮 Online Demo

🎯 Try it instantly on Hugging Face Spaces:
👉 Run SuperCCM Web App

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❇️ Installation

🧱 Option 1: From source

conda create -n superccm python=3.10 -y
conda activate superccm
pip install -r requirements.txt

📦 Option 2: From PyPI

pip install superccm

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⚡ Quick Start

✅ Using the default workflow

from superccm import DefaultWorkFlow

wf = DefaultWorkFlow()
metrics = wf.run('your/img/path')
print(metrics)

🧩 Shortcut (less formal)

from superccm.api import analysis

metrics = analysis('your/img/path')
print(metrics)

🌐 Launch the local web app

python app.py

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📖 Documentation

SuperCCM follows a simple and modular design philosophy, making it easy for both users and developers to get started quickly.

• 📘 Quick Tutorial: Learn how to use SuperCCM step-by-step
• 🧠 Module Development Guide: Integrate your own algorithms into the framework

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📄 License

This project is licensed under the GPL v3. You are free to use, modify, and distribute it under the same terms.

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🎓 Academic Reference

📢 Coming soon! Our manuscript has been accepted by TVST and will be available for citation soon.

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🧬 Made with ❤️ by the SuperCCM Team 💻 https://github.com/qlnfm/SuperCCM