MIST is a simple, fully automated framework for 3D medical imaging segmentation.
Project description
Medical Imaging Segmentation Toolkit
About
The Medical Imaging Segmentation Toolkit (MIST) is a simple, scalable, and end-to-end 3D medical imaging segmentation framework. MIST allows researchers to seamlessly train, evaluate, and deploy state-of-the-art deep learning models for 3D medical imaging segmentation.
MIST is licensed under CC BY-NC-SA 4.0. Please see the LICENSE file for more details.
Please cite the following papers if you use this code for your work:
What's New
- April 2024 - The Read the Docs page is up!
- March 2024 - Simplify and decouple postprocessing from main MIST pipeline.
- March 2024 - Support for using transfer learning with pretrained MIST models is now available.
- March 2024 - Boundary-based loss functions are now available.
- Feb. 2024 - MIST is now available as PyPI package and as a Docker image on DockerHub.
- Feb. 2024 - Major improvements to the analysis, preprocessing, and postprocessing pipelines, and new network architectures like UNETR added.
- Feb. 2024 - We have moved the TensorFlow version of MIST to mist-tf.
Documentation
We've moved our documentation over to Read the Docs. The Read the Docs page is here.
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