Metadata-Version: 2.1
Name: mist-medical
Version: 0.4.5a0
Summary: MIST is a simple, fully automated framework for 3D medical imaging segmentation.
Author: Rice University, The University of Texas MD Anderson Cancer Center
Author-email: Adrian Celaya <aecelaya@rice.edu>, David Fuentes <dtfuentes@mdanderson.org>, Beatrice Riviere <riviere@rice.edu>, Evan Lim <EMLim@mdanderson.org>, Rachel Glenn <rglenn1@mdanderson.org>, Alex Balsells <atb8@rice.edu>
License: Medical Imaging Segmentation Toolkit (MIST) (c) 2024 by Adrian Celaya
        is licenced under Attribution-NonCommercial-ShareAlike 4.0 International.
        To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/
Project-URL: homepage, https://github.com/aecelaya/MIST
Project-URL: repository, https://github.com/aecelaya/MIST
Keywords: deep learning,image segmentation,semantic segmentation,medical image analysis,medical image segmentation,nnU-Net,nnunet,U-Net,unet,vision transformers,UNETR,unetr
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: License :: Other/Proprietary License
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch==2.0.1
Requires-Dist: monai==1.3.0
Requires-Dist: antspyx==0.3.8
Requires-Dist: simpleitk==2.2.1
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: rich
Requires-Dist: tqdm
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: scikit-image
Requires-Dist: nvidia-dali-cuda110
Requires-Dist: tensorboard

Medical Imaging Segmentation Toolkit
===

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![Read the Docs](https://img.shields.io/readthedocs/mist-medical?style=flat)
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## 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](http://creativecommons.org/licenses/by-nc-sa/4.0/). Please see the [LICENSE](LICENSE) file for more details. 

Please cite the following papers if you use this code for your work:
 
[A. Celaya et al., "PocketNet: A Smaller Neural Network For Medical Image Analysis," in IEEE Transactions on Medical Imaging, doi: 10.1109/TMI.2022.3224873.](https://ieeexplore.ieee.org/document/9964128)

[A. Celaya et al., "FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging Segmentation", in Proceedings of LatinX in AI (LXAI) Research Workshop @ NeurIPS 2023, doi: 10.52591/lxai202312104](https://research.latinxinai.org/papers/neurips/2023/pdf/Adrian_Celaya.pdf)

## 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](https://github.com/aecelaya/mist-tf).

## Documentation
We've moved our documentation over to Read the Docs. The Read the Docs page is [**here**](https://mist-medical.readthedocs.io/).
