| Name | radtract JSON |
| Version |
0.2.3
JSON |
| download |
| home_page | None |
| Summary | Radiomic Tractometry for advanced along-tract analysis of diffusion-weighted MRI |
| upload_time | 2024-06-07 06:42:25 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.12,>=3.8 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. 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While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
| keywords |
tractometry
radiomics
tractography
diffusion-weighted mri
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<!--
Copyright © 2023 German Cancer Research Center (DKFZ), Division of Medical Image Computing
SPDX-License-Identifier: Apache-2.0
-->
# Radiomic Tractometry (RadTract)
Copyright © German Cancer Research Center (DKFZ), [Division of Medical Image Computing (MIC)](https://www.dkfz.de/en/mic/index.php). Please make sure that your usage of this code is in compliance with the code [license](https://github.com/MIC-DKFZ/radtract/blob/master/LICENSE).
If you use RadTract, please cite our [paper](https://www.nature.com/articles/s41467-023-44591-3): `Neher, P., Hirjak, D. & Maier-Hein, K. Radiomic tractometry reveals tract-specific imaging biomarkers in white matter. Nat Commun 15, 303 (2024). https://doi.org/10.1038/s41467-023-44591-3`
## Overview
RadTract is a python implementation of radiomic tractometry or "Tractomics". It is designed for tract-specific microstructural analysis of the brain’s white matter using diffusion MRI. It enhances traditional tractometry, which often misses valuable information due to its reliance on bare summary statistics and scalar values. RadTract incorporates radiomics, a method that analyzes a multitude of quantitative image features beyond visual perception, into tractometry. This integration allows for improved predictive modeling while maintaining the localization capability of tractometry.
RadTract has demonstrated its effectiveness in diagnosing disease subgroups across various datasets and estimating demographic and clinical parameters in multiple clinical populations. It holds the potential to pioneer a new generation of tract-specific imaging biomarkers, benefiting a wide range of applications from basic neuroscience to medical research.
For details about the approach, please refer to our [paper](https://www.nature.com/articles/s41467-023-44591-3): `Neher, P., Hirjak, D. & Maier-Hein, K. Radiomic tractometry reveals tract-specific imaging biomarkers in white matter. Nat Commun 15, 303 (2024). https://doi.org/10.1038/s41467-023-44591-3`. An overview of the method is shown in Figure 1.
_Figure 1: Illustration of the complete RadTract process. The points of a statically resampled tract (a) can be seen as samples of partly overlapping classes that are not linearly separable. We are aiming at finding the hyperplanes, superimposed as white lines on the tract in (a), that optimally separate the classes with the smallest amount of errors. This task can be solved using large-margin classifiers such as SVMs. This enables us to create parcellations directly in voxel-space (b) that do not suffer from projection-induced misassignments, as is the case in the centerline-based approach (d). For visualization purposes, the tract parcellation in voxel-space is projected back on the original streamlines (e). The proposed tract parcellation in voxel-space (b) is used to calculate a multitude of radiomics features per parcel, visualized in (c). Exemplary feature classes and image filters available when using [pyradiomics](https://pyradiomics.readthedocs.io/en/latest/) as calculation engine are listed in (f). RadTract currently supports [MIRP](https://github.com/oncoray/mirp) as an alternative engine for calculating radiomics features.
## Installation
### Requirements
- No specific hardware requirements. A state-of-the-art desktop computer should be sufficient.
- Tested on Ubuntu 22.04 but should run on other systems as well.
- Tested with Python 3.8 and higher
- Numpy should be installed prior to the RadTract setup (pyradiomics requirement), all other dependencies will be installed automatically.
- Should the pyradiomics setup fail with a missing numpy error despite installed numpy, see section "Pyradiomics installation issues" below.
- It is recommended to use a virtual environment for the installation.
See `.gitlab-ci.yml` for the currently tested configurations.
### Installation
Installation via anaconda is not supported currently!
1. virtual environment
- Create a virtual environment: `python -m venv myvenv`
- Activate the virtual environment: `source myvenv/bin/activate`
2. Installation
- Install from source: navigate to the root directory of RadTract and run `pip install .`
- Install from PyPI: run `pip install radtract`
Installation should complete within a few seconds.
### Pyradiomics installation issues
If the pyradiomics installation fails with a missing numpy error despite numpy being installed, a workaround is to install pyradimics directly from source:
1. Checkout the pyradiomics repo: `git clone git://github.com/Radiomics/pyradiomics`
2. Activate your virtual environment (if you use one): `source myvenv/bin/activate`
3. Navigate to the pyradiomics source and install from there: `pip install .`
4. Then run pip `pip install radtract` again.
## Examples
A complete pipeline example can be found in [example.ipynb](https://github.com/MIC-DKFZ/radtract/blob/main/example.ipynb).
Further examples can be found in the RadTract test script `tests\test_radtract.py`. Test data is included in `tests\test_data`.
### Expected runtimes
RadTract parcellation and feature calculation should complete within a couple of minutes on a standard desktop computer.
Raw data
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"description": "<!--\nCopyright \u00a9 2023 German Cancer Research Center (DKFZ), Division of Medical Image Computing\n\nSPDX-License-Identifier: Apache-2.0\n-->\n\n# Radiomic Tractometry (RadTract)\n\nCopyright \u00a9 German Cancer Research Center (DKFZ), [Division of Medical Image Computing (MIC)](https://www.dkfz.de/en/mic/index.php). Please make sure that your usage of this code is in compliance with the code [license](https://github.com/MIC-DKFZ/radtract/blob/master/LICENSE).\n\nIf you use RadTract, please cite our [paper](https://www.nature.com/articles/s41467-023-44591-3): `Neher, P., Hirjak, D. & Maier-Hein, K. Radiomic tractometry reveals tract-specific imaging biomarkers in white matter. Nat Commun 15, 303 (2024). https://doi.org/10.1038/s41467-023-44591-3`\n\n\n## Overview\n\nRadTract is a python implementation of radiomic tractometry or \"Tractomics\". It is designed for tract-specific microstructural analysis of the brain\u2019s white matter using diffusion MRI. It enhances traditional tractometry, which often misses valuable information due to its reliance on bare summary statistics and scalar values. RadTract incorporates radiomics, a method that analyzes a multitude of quantitative image features beyond visual perception, into tractometry. This integration allows for improved predictive modeling while maintaining the localization capability of tractometry.\n\nRadTract has demonstrated its effectiveness in diagnosing disease subgroups across various datasets and estimating demographic and clinical parameters in multiple clinical populations. It holds the potential to pioneer a new generation of tract-specific imaging biomarkers, benefiting a wide range of applications from basic neuroscience to medical research.\n\nFor details about the approach, please refer to our [paper](https://www.nature.com/articles/s41467-023-44591-3): `Neher, P., Hirjak, D. & Maier-Hein, K. Radiomic tractometry reveals tract-specific imaging biomarkers in white matter. Nat Commun 15, 303 (2024). https://doi.org/10.1038/s41467-023-44591-3`. An overview of the method is shown in Figure 1.\n\n_Figure 1: Illustration of the complete RadTract process. The points of a statically resampled tract (a) can be seen as samples of partly overlapping classes that are not linearly separable. We are aiming at finding the hyperplanes, superimposed as white lines on the tract in (a), that optimally separate the classes with the smallest amount of errors. This task can be solved using large-margin classifiers such as SVMs. This enables us to create parcellations directly in voxel-space (b) that do not suffer from projection-induced misassignments, as is the case in the centerline-based approach (d). For visualization purposes, the tract parcellation in voxel-space is projected back on the original streamlines (e). The proposed tract parcellation in voxel-space (b) is used to calculate a multitude of radiomics features per parcel, visualized in (c). Exemplary feature classes and image filters available when using [pyradiomics](https://pyradiomics.readthedocs.io/en/latest/) as calculation engine are listed in (f). RadTract currently supports [MIRP](https://github.com/oncoray/mirp) as an alternative engine for calculating radiomics features.\n\n## Installation\n\n### Requirements\n\n- No specific hardware requirements. A state-of-the-art desktop computer should be sufficient.\n- Tested on Ubuntu 22.04 but should run on other systems as well.\n- Tested with Python 3.8 and higher\n- Numpy should be installed prior to the RadTract setup (pyradiomics requirement), all other dependencies will be installed automatically. \n- Should the pyradiomics setup fail with a missing numpy error despite installed numpy, see section \"Pyradiomics installation issues\" below.\n- It is recommended to use a virtual environment for the installation. \n\nSee `.gitlab-ci.yml` for the currently tested configurations.\n\n### Installation\n\nInstallation via anaconda is not supported currently!\n\n1. virtual environment\n - Create a virtual environment: `python -m venv myvenv`\n - Activate the virtual environment: `source myvenv/bin/activate`\n2. Installation\n - Install from source: navigate to the root directory of RadTract and run `pip install .`\n - Install from PyPI: run `pip install radtract`\n\nInstallation should complete within a few seconds.\n\n### Pyradiomics installation issues\n\nIf the pyradiomics installation fails with a missing numpy error despite numpy being installed, a workaround is to install pyradimics directly from source:\n\n1. Checkout the pyradiomics repo: `git clone git://github.com/Radiomics/pyradiomics`\n2. Activate your virtual environment (if you use one): `source myvenv/bin/activate`\n3. Navigate to the pyradiomics source and install from there: `pip install .`\n4. Then run pip `pip install radtract` again.\n\n## Examples\n\nA complete pipeline example can be found in [example.ipynb](https://github.com/MIC-DKFZ/radtract/blob/main/example.ipynb). \n\nFurther examples can be found in the RadTract test script `tests\\test_radtract.py`. Test data is included in `tests\\test_data`.\n\n\n### Expected runtimes\n\nRadTract parcellation and feature calculation should complete within a couple of minutes on a standard desktop computer.\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. 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Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets \"[]\" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same \"printed page\" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ",
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