{"url":"/dataset/deepmtj","name":"deepMTJ","full_name":"Muscle-Tendon Junction Tracking in Ultrasound Images","description_markdown":"[![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)\r\n\r\n\r\ndeepMTJ: Muscle-Tendon Junction Tracking in Ultrasound Images\r\n-------------------------------------------------------------\r\n\r\n`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.\r\n\r\n\r\nIntroduction into the deepMTJ dataset\r\n-------------------------------------\r\n\r\nThis 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).\r\n\r\n- 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). \r\n\r\n- 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.","description_withheld":null,"homepage":"https://osf.io/wgy4d/","introduced_date":"2021-11-24","introduced_date_note":null,"introduced_by":null,"license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Muscle Tendon Junction Identification","url":"/task/muscle-tendon-junction-identification","datasets_with_task":"/datasets/task/muscle-tendon-junction-identification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["deepMTJ"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/muscle-tendon-junction-identification-on-2","task":"Muscle Tendon Junction Identification","dataset_variant":"deepMTJ","rows":1,"metrics":["RMSE"],"first_row_in_archive_order":{"model":"deepMTJ_IEEEtbme_version_2021","paper":"/paper/a-human-centered-machine-learning-approach-1","metrics":{"RMSE":"4.89 mm"},"code_links":[{"title":"luuleitner/deepMTJ","url":"https://github.com/luuleitner/deepMTJ"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-human-centered-machine-learning-approach-1","title":"A Human-Centered Machine-Learning Approach for Muscle-Tendon Junction Tracking in Ultrasound Images","date":"2022-02-10","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}