DeepACSA
Automatic analysis of human lower limb ultrasonography images
DeepACSA is an open-source tool to evaluate the anatomical cross-sectional area of muscles in ultrasound images using deep learning. More information about the installtion and usage of DeepACSA can be found in the online documentation. You can find information about contributing, issues and bug reports there as well. If you find this work useful, please remember to cite the corresponding paper, where more information about the model architecture and performance can be found as well.
Quickstart
To quickly start the DeepACSA either open the executable or type
python -m Deep_ACSA
in your prompt once the package was installed and the DeepACSA environment activated.
1 maintainer
Metadata
1 maintainer
Release files for DeepACSA 0.3.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| deepacsa-0.3.1.tar.gz | 81.3 kB | Without provenance | Details for deepacsa-0.3.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| deepacsa-0.3.1-py3-none-any.whl | Python 3 | none | any | Details for deepacsa-0.3.1-py3-none-any.whl |
Total release size: 132.1 kB
Release files / deepacsa-0.3.1.tar.gz
| Download URL | deepacsa-0.3.1.tar.gz |
|---|---|
| Size | 81.3 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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No |
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twine/4.0.2 CPython/3.9.18
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Release files / deepacsa-0.3.1-py3-none-any.whl
| Download URL | deepacsa-0.3.1-py3-none-any.whl |
|---|---|
| Size | 50.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
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twine/4.0.2 CPython/3.9.18
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Security for DeepACSA 0.3.1
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