Omkar2118/datasets huggingface dataset:
deepMTJ
Full name: Muscle-Tendon Junction Tracking in Ultrasound Images

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/luuleitner/deepMTJ/blob/master/mtj_tracking/predict/mtj_tracking.ipynb)


deepMTJ: Muscle-Tendon Junction Tracking in Ultrasound Images
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`deepMTJ` is a machine learning approach for automatically tracking of muscle-tendon junctions (MTJ) in ultrasound images. Our method is based on a convolutional neural network trained to infer MTJ positions across various ultrasound systems from different vendors, collected in independent laboratories from diverse observers, on distinct muscles and movements. We built `deepMTJ` to support clinical biomechanists and locomotion researchers with an open-source tool for gait analyses.


Introduction into the deepMTJ dataset
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This repository contains the full test dataset used for `deepMTJ` performance assessments, the trained TensorFlow (Keras) model and a Link to the code repository of deepMTJ. Furthermore, we provide online predictions using `deepMTJ` via a [![Colab Notebook](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/luuleitner/deepMTJ/blob/master/mtj_tracking/predict/mtj_tracking.ipynb) (For multiple and large file predictions) and via [deepmtj.org](https://deepmtj.org/) (Cloud based predictions).

- The dataset comprises 1344 images of muscle-tendon junctions recorded with 3 ultrasound imaging systems (Aixplorer V6, Esaote MyLab60, Telemed ArtUs), on 2 muscles (Lateral Gastrocnemius, Medial Gastrocnemius), and 2 movements (isometric maximum voluntary contractions, passive torque movements). 

- We have included the ground truth labels for each image. These reference labels are the computed mean from 4 specialist labels. Specialist annotators had 2-10 years of experience in biomechanical and clinical research investigating muscles and tendons in 2-9 ultrasound studies in the past 2 years.