SegDicom

Small and lightweight web app learning project for case creation with DICOM series that can be segmented with image processing algorithms.

Modules

• Angular web app
• .NET API
• Python Azure Function
• Azurite Blob Storage

Technology Stack

• Angular 17 for the frontend web app
  • TypeScript for strongly typed JavaScript
  • Vite for optimized builds and efficient HMR
  • 🌐 ngx-translate for maintainable internationalization in:
    • 🇨🇦 English
    • 🇫🇷 French
    • 🇯🇵 Japanese
    • 🇻🇳 Vietnamese
  • 🩻 dicom.ts for lightning fast DICOM rendering
  • 🌑 Dark mode support
• .NET 8.0 for the C# backend API
  • Entity Framework for in-memory database
  • OpenAPI for an comprehensible API description.
  • Fellow Oak DICOM for effortless DICOM tag parsing
• Azure for cloud computing
  • Azurite for Azure Blob Storage emulation
  • Azure Function to execute event-driven serverless python code
    • Python 3.11 for DICOM segmentation with Image Processing techniques
      • imageio for image read and write operations with various format
      • NumPy for n-dimensional image data array manipulation
      • Pydicom for DICOM image open/read operations
      • scikit-image for modern image processing
      • Segmentation Algorithm Source: Visualizing and Analyzing DICOMs in Python
• ✨ README Template Inspiration: Full Stack FastAPI Template

How to Use it

Requirements

• Node
• npm
• .NET SDK
• Python
• Visual Studio Code
• Azure Storage Explorer

Do not forget to add angular, node and python paths to PATH environment variables.

Add the following lines in the user secrets of the .NET project:

{
  "AzureDicomStorage": "UseDevelopmentStorage=true",
  "AzureDicomStorage:blob": "UseDevelopmentStorage=true"
}

Installation

To install all necessary dependencies and libraries, please execute the following command in the project directory:

./install.sh

Note: Each module necessary dependencies and libraries can be installed individually by executing the corresponding scripts in the the scripts/install folder

Start

To start the application, please execute the following command in the project directory:

./run.sh

Note:

• Each module can be run individually by executing the corresponding scripts in the the scripts/run folder
• The Angular app will run on http://localhost:4200
• The .NET API will run on https://localhost:6900 (with Swagger on https://localhost:6900/swagger)
• The Python Azure Function will run on http://localhost:6660/api/segment
• The Azurite Blob Storage content can be monitored with Azure Storage Explorer