MétaCan
Menu
Back to cohort
Record W4393495729 · doi:10.5281/zenodo.7740734

Freehand ultrasound without external trackers

2022· dataset· en· W4393495729 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languageen
FieldEngineering
TopicEngineering Technology and Methodologies
Canadian institutionsnot available
Fundersnot available
KeywordsBitTorrent trackerUltrasoundComputer scienceComputer visionComputer graphics (images)Artificial intelligenceMedicineRadiologyEye tracking

Abstract

fetched live from OpenAlex

We have collected a new large freehand ultrasound dataset and are organising a MICCAI2024&2025 Challenges (TUS-REC Challenge). Check Part 1 and Part 2 of the training dataset for TUS-REC2024, and Train Data for TUS-REC2025. Freehand US scans were acquired on both left and right forearms from 19 volunteers, using Ultrasonix machine (BK, Europe) with a curvilinear probe (4DC7-3/40), tracked by an NDI Polaris Vicra (Northern Digital Inc., Canada). On each forearm, the US probe was moved, for the study purpose, in a straight line, a ‘C’ shape and a ‘S’ shape, in a distal-to-proximal direction. These three scans were repeated, with the curvilinear transducer held (thus the US planes) perpendicular of and parallel to the forearm. B-mode images with median level of speckle reduction were recorded at ~20 fps. Each scan included frames between 36 and 430 with a size of 480×640 pixels, equivalent to a probe travel distance approximately between 100 and 200 mm. A total of 12 scans were acquired from each volunteer, recorded in a single ‘*.mha’ file, with the filename indicating the acquisition time. For example, “LH_Ver_S_20220425_141454.mha” means a scan acquired on 14:14:54 April 25th, 2022. The ‘valid_frames.csv’ file contains the 6 “protocols” with each arm from each volunteer: 1) RH_Par_L (right arm, straight line shape with the probe parallel to the forearm); 2) RH_Par_C (right arm, ‘C’ shape with the probe parallel to the forearm); 3) RH_Par_S (right arm, ‘S’ shape with the probe parallel to the forearm); 4) RH_Ver_L (right arm, straight line shape with the probe perpendicular of the forearm); 5) RH_Ver_C (right arm, ‘C’ shape with the probe perpendicular of the forearm); 6) RH_Ver_S (right arm, ‘S’ shape with the probe perpendicular of the forearm); 7) LH_Par_L (left arm, straight line shape with the probe parallel to the forearm); 8) LH_Par_C (left arm, ‘C’ shape with the probe parallel to the forearm); 9) LH_Par_S (left arm, ‘S’ shape with the probe parallel to the forearm); 10) LH_Ver_L (left arm, straight line shape with the probe perpendicular of the forearm); 11) LH_Ver_C (left arm, ‘C’ shape with the probe perpendicular of the forearm); 12) LH_Ver_S (left arm, ‘S’ shape with the probe perpendicular of the forearm). The ‘start’ and ‘end’ denote the start and end frame indices of a scan, respectively, in each ‘*.mha’ file. US images, transformation matrix obtained from the tracker, and corresponding csv file, for each scan can be found in Freehand_US_data.zip. In the “calib_matrix.csv” file, we provide a calibration matrix and a time difference in sec, obtained from our calibration experiments. A baseline code is provided in this repo. If you find this data set useful for your research, please consider citing some of the following works: Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames." In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1-5. IEEE, 2023. doi: 10.1109/ISBI53787.2023.10230773 Qi Li, Ziyi Shen, Qianye Yang, Dean C. Barratt, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Nonrigid Reconstruction of Freehand Ultrasound without a Tracker." In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 689-699. Cham: Springer Nature Switzerland, 2024. doi: 10.1007/978-3-031-72083-3_64 Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker." IEEE Transactions on Biomedical Engineering, vol. 71, no. 3, pp. 1033-1042, 2024. doi: 10.1109/TBME.2023.3325551. Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction." In International Workshop on Advances in Simplifying Medical Ultrasound, pp. 142-151. Cham: Springer Nature Switzerland, 2023. doi: https://doi.org/10.1007/978-3-031-44521-7_14

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0770.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.255
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it