For more information on Retuve, see https://github.com/radoss-org/retuve
This codebase has the TinyUnet AI Plugin for Retuve, which uses Radiopedia data from The Open Hip Dataset to train.
The model weights are strictly under the terms of the CC BY-NC-SA 3.0 license. This is because the model is trained on Radiopedia Data, which is under the CC BY-NC-SA 3.0 license.
This means that you cannot use this codebase for any commercial purposes and you must attribute Radiopedia for the data used to train the model.
The codes for the licence can be found in the LICENSE file.
To install the plugin, you can use the following command:
pip install git+https://github.com/radoss-org/retuve-tinyunet-plugin.gitPlease see https://github.com/radoss-org/retuve/tree/main/examples for more examples. This is purely meant to illustrate how to use the plugin.
import pydicom
from retuve.defaults.hip_configs import default_US
from retuve.funcs import analyse_hip_3DUS
from retuve.testdata import Cases, download_case
from retuve_tinyunet_plugin.ultrasound import tinyunet_predict_dcm_us
# Get an example case
dcm_file = download_case(Cases.ULTRASOUND_DICOM)[0]
default_US.device = "cpu"
dcm = pydicom.dcmread(dcm_file)
hip_datas, *_ = analyse_hip_3DUS(
dcm,
keyphrase=default_US,
modes_func=tinyunet_predict_dcm_us,
modes_func_kwargs_dict={},
)
print(hip_datas)We give full attribution to the authors that made this effort possible on Radiopedia. The list of these authors can be found here.
The codes for the licence can be found in the LICENSE file.
If you are interested in a less-restritive licence, the first step is to contact Radiopedia for a special licence to use all the data this model is trained on. That list can be found here.
RadOSS will then consider providing you a commercial licence for this plugin at no charge. Please contact us at info@radoss.org when you have obtained the licence from Radiopedia.
If you use this plugin, please cite the following:
@InProceedings{Chen_TinyUNet_MICCAI2024,
author = {Chen, Junren and Chen, Rui and Wang, Wei and Cheng, Junlong and Zhang, Lei and Chen, Liangyin},
title = {TinyU-Net: Lighter Yet Better U-Net with Cascaded Multi-receptive Fields},
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
year = {2024},
publisher = {Springer Nature Switzerland},
volume = {LNCS 15009},
month = {October},
pages = {626--635}
}
@misc{radiopaedia_ddh_cases,
author = {Sheikh, Yusra and Thibodeau, Ryan and Ranchod, Ashesh Ishwarlal and
Hisham},
title = {Radiopaedia cases of Developmental Dysplasia of the Hip},
year = {2023-2024},
howpublished = {\url{https://radiopaedia.org/}},
note = {Cases: 72628 (Yusra Sheikh), 172535-172536, 172658, 172534, 171555-171556, 172533, 171551, 171553-171554 (Ryan Thibodeau), 167854-167855, 167857 (Ashesh Ishwarlal Ranchod), 56568 (Hisham Alwakkaa); Accessed: [Date of access]}
}