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@LalithShiyam LalithShiyam released this 10 Sep 21:20
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Learn about vigilant mode.

🎉 Announcing MOOSE 2.0: Leaner. Meaner. Stronger 🎉

Exciting times are ahead! We're thrilled to unveil MOOSE 2.0, taking 3D medical image segmentation to unprecedented heights! 🚀

🌟 Features at a Glance:

  • Leaner: Optimized for efficiency, MOOSE 2.0 doesn't demand extensive resources. It's compatible with various OS and even works without high-end GPUs (a bit slower though).

  • Meaner: A remarkable speed upgrade – we're talking about a version that's 5x faster than version 1! Designed for both clinical and preclinical (coming soon) settings, this is a segmentation powerhouse. ⚡

  • Stronger: The strength of MOOSE 2.0 is undeniable, backed by Data-centric AI principles and a staggering 2.5k datasets.

  • Versatility: Whether you prefer command-line tools for batch processing or using it as a library for your Python projects, MOOSE 2.0 offers unmatched flexibility. 😎

📌 Ready to Dive In?
Before you start, ensure you meet the requirements:

  • OS Compatibility: Windows, Mac, or Linux.
  • Memory: At least 32GB RAM.
  • GPU: For enhanced speed, an NVIDIA GPU is recommended.
  • Python: Version 3.9 or above.

🔧 Quick Installation:

  • For Linux and MacOS:

    python3 -m venv moose-env
source moose-env/bin/activate
pip install moosez

  • For Windows:

    python -m venv moose-env
.\moose-env\Scripts\activate
pip install moosez

✨ How to Use:

  • As a Command-Line Tool:

    moosez -d <path_to_image_dir> -m <model_name>

  • As a Library in Python Projects:

    from moosez import moose
moose(model_name, input_dir, output_dir, accelerator)

📂 Adherence to the specified directory structure and naming conventions is crucial for the best results with MOOSE 2.0.

🎁 Contribute to MooseZ:
Join the MooseZ community! Add your custom nnUNetv2 models to MooseZ and enjoy the speed and efficiency it offers.

🔍 The 'Z' in our Python Packages:
Our signature 'Z' is a testament to our innovative spirit at QIMP. It signifies our quest for the unknown, always pushing the boundaries in medical imaging.

Dive into the complete README for a detailed exploration. Here's to redefining the future of medical image segmentation! Join us in this exhilarating journey with MOOSE 2.0. 🚀🔬

Happy segmenting! 💡🎊

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