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](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/).
Raw data
{
"_id": null,
"home_page": null,
"name": "mist-medical",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "deep learning, image segmentation, semantic segmentation, medical image analysis, medical image segmentation, nnU-Net, nnunet, U-Net, unet, vision transformers, UNETR, unetr",
"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>",
"download_url": "https://files.pythonhosted.org/packages/5e/42/b5dc629305f7baf38bd566dcfc077356cde4d15acccaee7f2fe7de112b2f/mist_medical-0.4.6a0.tar.gz",
"platform": null,
"description": "Medical Imaging Segmentation Toolkit\n===\n\n\n\n\n\n\n\n\n## About\nThe Medical Imaging Segmentation Toolkit (MIST) is a simple, scalable, and end-to-end 3D medical imaging segmentation \nframework. MIST allows researchers to seamlessly train, evaluate, and deploy state-of-the-art deep learning models for 3D \nmedical imaging segmentation.\n\nMIST 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. \n\nPlease cite the following papers if you use this code for your work:\n \n[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)\n\n[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)\n\n## What's New\n* April 2024 - The Read the Docs page is up!\n* March 2024 - Simplify and decouple postprocessing from main MIST pipeline.\n* March 2024 - Support for using transfer learning with pretrained MIST models is now available.\n* March 2024 - Boundary-based loss functions are now available.\n* Feb. 2024 - MIST is now available as PyPI package and as a Docker image on DockerHub.\n* Feb. 2024 - Major improvements to the analysis, preprocessing, and postprocessing pipelines, \nand new network architectures like UNETR added.\n* Feb. 2024 - We have moved the TensorFlow version of MIST to [mist-tf](https://github.com/aecelaya/mist-tf).\n\n## Documentation\nWe've moved our documentation over to Read the Docs. The Read the Docs page is [**here**](https://mist-medical.readthedocs.io/).\n",
"bugtrack_url": null,
"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/",
"summary": "MIST is a simple, fully automated framework for 3D medical imaging segmentation.",
"version": "0.4.6a0",
"project_urls": {
"homepage": "https://github.com/aecelaya/MIST",
"repository": "https://github.com/aecelaya/MIST"
},
"split_keywords": [
"deep learning",
" image segmentation",
" semantic segmentation",
" medical image analysis",
" medical image segmentation",
" nnu-net",
" nnunet",
" u-net",
" unet",
" vision transformers",
" unetr",
" unetr"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "de3cd89636c411e4e14a7130f332813685ae3a8be0cbd2b8f0bc6852247d5fe3",
"md5": "67b07a13702d958211c12ff420328a2f",
"sha256": "dc8e7d7d1337f0b19c76984cd38be8f4731e1b20c0cdfdcb1081f16a395e3667"
},
"downloads": -1,
"filename": "mist_medical-0.4.6a0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "67b07a13702d958211c12ff420328a2f",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 68707,
"upload_time": "2024-04-24T19:44:23",
"upload_time_iso_8601": "2024-04-24T19:44:23.807463Z",
"url": "https://files.pythonhosted.org/packages/de/3c/d89636c411e4e14a7130f332813685ae3a8be0cbd2b8f0bc6852247d5fe3/mist_medical-0.4.6a0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "5e42b5dc629305f7baf38bd566dcfc077356cde4d15acccaee7f2fe7de112b2f",
"md5": "fa23b01095b1d87ee1d7a285fb1bdd20",
"sha256": "f4291d633e69ae4cbd6ad577e14afe463c386d198369275af4c5af6efbf3b57d"
},
"downloads": -1,
"filename": "mist_medical-0.4.6a0.tar.gz",
"has_sig": false,
"md5_digest": "fa23b01095b1d87ee1d7a285fb1bdd20",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 56196,
"upload_time": "2024-04-24T19:44:25",
"upload_time_iso_8601": "2024-04-24T19:44:25.089379Z",
"url": "https://files.pythonhosted.org/packages/5e/42/b5dc629305f7baf38bd566dcfc077356cde4d15acccaee7f2fe7de112b2f/mist_medical-0.4.6a0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-04-24 19:44:25",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "aecelaya",
"github_project": "MIST",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "mist-medical"
}
JSON
