Skip to main content

DL_Track_US

DOI

The DL_Track_US package provides an easy to use graphical user interface (GUI) for deep learning based analysis of muscle architectural parameters from longitudinal ultrasonography images of human lower limb muscles. Please take a look at our documentation for more information (note that aggressive ad-blockers might break the visualization of the repository description as well as the online documentation). This code is based on a previously published algorithm and replaces it. We have extended the functionalities of the previously proposed code. The previous code will not be updated and future updates will be included in this repository.

Getting started

For detailled information about installaion of the DL_Track_US python package we refer you to our documentation. There you will finde guidelines not only for the installation procedure of DL_Track_US, but also concerding conda and GPU setup.

Quickstart

Once installed, DL_Track_US can be started from the command prompt with the respective environment activated:

(DL_Track_US0.3.0) C:/User/Desktop/ python -m DL_Track_US

In case you have downloaded the executable, simply double-click the DL_Track_US icon.

Regardless of the used method, the GUI should open. For detailed the desciption of our GUI as well as usage examples, please take a look at the user instruction. An illustration of out GUI start window is presented below. It is here where users must specify input directories, choose the preferred analysis type, specify the analysis parameters or train thrain their own neural networks based on their own training data.

GUI

Testing

We have not yet integrated unit testing for DL_Track_US. Nonetheless, we have provided instructions to objectively test whether DL_Track_US, once installed, is functionable. To perform the testing procedures yourself, check out the test instructions.

Code documentation

In order to see the detailled scope and description of the modules and functions included in the DL_Track_US package, you can do so either directly in the code, or in the Documentation section of our online documentation.

Community guidelines

Wheter you want to contribute, report a bug or have troubles with the DL_Track_US package, take a look at the provided instructions how to best do so.

Release files for DL-Track-US 0.3.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for DL-Track-US 0.3.0
File Size Uploaded
dl_track_us-0.3.0.tar.gz 44.9 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for DL-Track-US 0.3.0
File Interpreter ABI Platform
dl_track_us-0.3.0-py3-none-any.whl Python 3 none any Details

Total release size: 89.6 MB

Release files / dl_track_us-0.3.0.tar.gz

Download URL dl_track_us-0.3.0.tar.gz
Size 44.9 MB
Tags Source
SHA-256 checksum
How to use checksums
0bcb00c5eef48e50c2b3512bf9ea65d713cb24213401ee68eec32256e92949a6
BLAKE2b-256 checksum
How to use checksums
82e83f59a4ff9de191c0f12ee5816b2ff61e64552e284c580a1578ca2c8de282
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.10.16

Release files / dl_track_us-0.3.0-py3-none-any.whl

Download URL dl_track_us-0.3.0-py3-none-any.whl
Size 44.7 MB
Tags Python 3
SHA-256 checksum
How to use checksums
22ec47c6b348260e8c82e8cc5efd13b8aa5c687112cfce9e3c4ac1b245da4b4f
BLAKE2b-256 checksum
How to use checksums
f2ae650be37ea549df7b1af24544570649192d318cfc71709e084f66ea76d83c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.10.16

Release history Release notifications | RSS feed

This release

0.3.0 This release

2 release files

0.2.1

2 release files

0.1.2

2 release files

0.1.1

2 release files

0.1.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page