In 2023, over 34 percent of mothers in Italy had between four and six ultrasounds during their pregnancy, while 42.3 percent had seven or more. This statistic shows the distribution of ultrasounds before childbirth in Italy in 2023, by number of ultrasounds.

The market value of ultrasound devices in Latin America is forecast to reach approximately one billion U.S. dollars by 2028, up from an estimated 743 million U.S. dollars in 2023. A compound annual growth rate (CAGR) of around 6.93 percent is expected for the period.

Access the summary of the Ultrasound market report, featuring key insights, executive summary, market size, CAGR, growth rate, and future outlook.

Fetal ultrasound dataset (CRL)

This statistic illustrates the number of ultrasound machines in the healthcare facilities in Italy from 2014 to 2016. According to data, the number of appliances utilized to perform ultrasound scans increased. As of 2016, the ultrasound machines were 37,521.

The ultrasound market is set to be valued at US$ 11.70 billion in 2024 and is projected to reach US$ 20.40 billion by 2034, exhibiting a modest CAGR of 5.80% over the forecast period.

Attributes	Key Statistics
Projected Market Value (2024)	US$ 11.70 billion
Estimated Market Value (2034)	US$ 20.40 billion
Forecasted Growth Rate (2024 to 2034)	5.80% CAGR

Category-wise Insights

Attributes	Details
Component	Ultrasonic Devices
Forecasted CAGR (From 2024 to 2034)	4.20%

Attributes	Details
Technology	2D Ultrasounds
Forecasted CAGR (From 2024 to 2034)	4.10%

Country-wise Insights

Countries	Forecasted CAGR (From 2024 to 2034)
South Korea	6.80%
The United Kingdom	6.60%
China	6.40%
The United States	5.90%
Japan	5.00%

About the dataAn ultrasound dataset to use in the discovery of ultrasound features associated with pain and radiographic change in KOA is highly innovative and will be a major step forward for the field. These ultrasound images originate from the diverse and inclusive population-based Johnston County Health Study (JoCoHS). This dataset is designed to adhere to FAIR principles and was funded in part by an Administrative Supplement to Improve the AI/ML-Readiness of NIH-Supported Data (3R01AR077060-03S1). This dataset includes a subset of JoCoHS participants who underwent ultrasound imaging and radiographic evaluations. To begin learning about the dataset, visit our User Guide for an all-in-one document containing statistics and other details to help you work with the data.

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of early deaths. The data reviews the medical images of breast cancer using ultrasound scan. Breast Ultrasound Dataset is categorized into three classes: normal, benign, and malignant images. Breast ultrasound images can produce great results in classification, detection, and segmentation of breast cancer when combined with machine learning.

Data The data collected at baseline include breast ultrasound images among women in ages between 25 and 75 years old. This data was collected in 2018. The number of patients is 600 female patients. The dataset consists of 780 images with an average image size of 500*500 pixels. The images are in PNG format. The ground truth images are presented with original images. The images are categorized into three classes, which are normal, benign, and malignant.

If you use this dataset, please cite: Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A. Dataset of breast ultrasound images. Data in Brief. 2020 Feb;28:104863. DOI: 10.1016/j.dib.2019.104863.

Background: The purpose of this study was twofold: to assess the status of undergraduate medical ultrasound (US) education in the German-speaking area and to suggest a possible framework for a longitudinal undergraduate medical US curriculum based on the study results and a literature review. Methods: The survey included 44 medical faculties in the German-speaking area: 37 in Germany, four in Austria and three in German-speaking Switzerland. A standardized questionnaire focused on the following aspects of undergraduate medical US education: general information, organization, resources, assessment methods and evaluation. Results: Data from 28 medical faculties were analysed. 26 out of 28 medical faculties offered US courses, 21 offered compulsory as well as elective courses, four offered compulsory and one elective courses only. 27 medical faculties supported US skills implementation. Abdominal US (n=25) was most common in teaching basic US skills. A learning objective catalogue was provided at 15 medical faculties. At 22 medical faculties, medical specialists were involved in undergraduate medical US education. 24 out of 26 medical faculties thought that peer-teaching is important to convey US skills. Medical faculties used the following methods to assess US skills: objective structured clinical examination (OSCE, n=7), non-standardized practical exams (n=4), non-standardized combined oral-practical exams (n=2) or direct observation of procedural skills (DOPS, n=1). 25 out of 26 medical faculties evaluated their US courses and 19 made suggestions for improvements in undergraduate medical US education. Conclusion: Medical faculty members in the German-speaking area have recognized the relevance of undergraduate medical US education. So far, courses are offered heterogeneously with rather short hands-on scanning time and high student-instructor ratio. Based on the results of this study and a literature review we suggest a possible framework and milestones on the way to a longitudinal undergraduate medical US curriculum.

Access the summary of the Ultrasound Transducer market report, featuring key insights, executive summary, market size, CAGR, growth rate, and future outlook.

DukeUltrasound is an ultrasound dataset collected at Duke University with a Verasonics c52v probe. It contains delay-and-sum (DAS) beamformed data as well as data post-processed with Siemens Dynamic TCE for speckle reduction, contrast enhancement and improvement in conspicuity of anatomical structures. These data were collected with support from the National Institute of Biomedical Imaging and Bioengineering under Grant R01-EB026574 and National Institutes of Health under Grant 5T32GM007171-44. A usage example is available here.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('duke_ultrasound', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

This dataset contains a collection of ultrafast ultrasound acquisitions from nine volunteers and the CIRS 054G phantom. For a comprehensive understanding of the dataset, please refer to the paper: Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning. J. Imaging 2023, 9, 256. https://doi.org/10.3390/jimaging9120256. Please cite the original paper when using this dataset.

Due to data size restriction, the dataset has been divided into six subdatasets, each one published into a separate entry in Zenodo. This repository contains subdataset 3.

Structure

In Vivo Data

• Number of Acquisitions: 20,000

• Volunteers: Nine volunteers

• File Structure: Each volunteer's data is compressed in a separate zip file.

  • Note: For volunteer 1, due to a higher number of acquisitions, data for this volunteer is distributed across multiple zip files, each containing acquisitions from different body regions.

• Regions :

  • Abdomen: 6599 acquisitions
  • Neck: 3294 acquisitions
  • Breast: 3291 acquisitions
  • Lower limbs: 2616 acquisitions
  • Upper limbs: 2110 acquisitions
  • Back: 2090 acquisitions

• File Naming Convention: Incremental IDs from acquisition_00000 to acquisition_19999.

In Vitro Data

• Number of Acquisitions: 32 from CIRS model 054G phantom
• File Structure: The in vitro data is compressed in the cirs-phantom.zip file.
• File Naming Convention: Incremental IDs from invitro_00000 to invitro_00031.

CSV Files

Two CSV files are provided:

• invivo_dataset.csv :

  • Contains a list of all in vivo acquisitions.
  • Columns: id, path, volunteer id, body region.

• invitro_dataset.csv :

  • Contains a list of all in vitro acquisitions.
  • Columns: id, path

Zenodo dataset splits and files

The dataset has been divided into six subdatasets, each one published in a separate entry on Zenodo. The following table indicates, for each file or compressed folder, the Zenodo dataset split where it has been uploaded along with its size. Each dataset split is named "A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (ii/6)", where ii represents the split number. This repository contains the 3rd split.

File name	Size	Zenodo subdataset number
invivo_dataset.csv	995.9 kB	1
invitro_dataset.csv	1.1 kB	1
cirs-phantom.zip	418.2 MB	1
volunteer-1-lowerLimbs.zip	29.7 GB	1
volunteer-1-carotids.zip	8.8 GB	1
volunteer-1-back.zip	7.1 GB	1
volunteer-1-abdomen.zip	34.0 GB	2
volunteer-1-breast.zip	15.7 GB	2
volunteer-1-upperLimbs.zip	25.0 GB	3
volunteer-2.zip	26.5 GB	4
volunteer-3.zip	20.3 GB	3
volunteer-4.zip	24.1 GB	5
volunteer-5.zip	6.5 GB	5
volunteer-6.zip	11.5 GB	5
volunteer-7.zip	11.1 GB	6
volunteer-8.zip	21.2 GB	6
volunteer-9.zip	23.2 GB	4

Normalized RF Images

• Beamforming:

  • Depth from 1 mm to 55 mm

  • Width spanning the probe aperture

  • Grid: 𝜆/8 × 𝜆/8

  • Resulting images shape: 1483 × 1189

  • Two beamformed RF images from each acquisition:

    • Input image: single unfocused acquisition obtained from a single plane wave (PW) steered at 0° (acquisition-xxxx-1PW)
    • Target image: coherently compounded image from 87 PWs acquisitions steered at different angles (acquisition-xxxx-87PWs)

• Normalization:

  • The two RF images have been normalized

• To display the images:

  • Perform the envelop detection (to obtain the IQ images)
  • Log-compress (to obtain the B-mode images)

• File Format: Saved in npy format, loadable using Python and numpy.load(file).

Training and Validation Split in the paper

For the volunteer-based split used in the paper:

• Training set: volunteers 1, 2, 3, 6, 7, 9
• Validation set: volunteer 4
• Test set: volunteers 5, 8
• Images analyzed in the paper
  • Carotid acquisition (from volunteer 5): acquisition_12397
  • Back acquisition (from volunteer 8): acquisition_19764
  • In vitro acquisition: invitro-00030

License

This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Please cite the original paper when using this dataset :

Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning. J. Imaging 2023, 9, 256. DOI: 10.3390/jimaging9120256

Contact

For inquiries or issues related to this dataset, please contact:

• Name: Roser Viñals
• Email: roser.vinalsterres@epfl.ch

A total of 227 cross sectional images (20 x 54 mm with a resolution of 289 x 648 pixels) of hind-leg xenograft tumors from 29 mice were obtained with 1mm step-wise movement of the array mounted on a manual positioning device. The whole tumor volume was acquired using a diagnostic ultrasound system with a 10 MHz linear transducer and 50 MHz sampling. The unprocessed ultrasound radio-frequency data can be downloaded along with the liver tumor segmentation masks form http://omar.alkadi.net/767-2/.

The dataset images are in Analyze 7.5 file format consisting of two files: a header with information about the size and number of voxels in each dimension (filename extension .hdr), and the actual data in binary format (filename extension .img). The images in the dataset are formatted according to the following protocol: CASE NUMBER | FILE TYPE (LOC OR SEG) | FILE EXTENSION | where loc is the original image and seg is the associated segmentation mask. The volumetric data are categorised into progressive versus non-progressive cases in response to chemotherapy treatment.

Continuous imaging of internal organs over days could provide crucial information about health and diseases and enable insights into developmental biology. We report a bioadhesive ultrasound (BAUS) device, which consists of a thin and rigid ultrasound probe robustly adhered on the skin via a couplant of a soft, tough, anti-dehydrating and bioadhesive hydrogel-elastomer hybrid. The BAUS device provides 48-hour continuous imaging of diverse internal organs including blood vessels, muscle, heart, gastrointestinal tract, diaphragm, and lung. The BAUS device could enable diagnostic and monitoring tools for various diseases.

Access the summary of the Therapeutic Ultrasound market report, featuring key insights, executive summary, market size, CAGR, growth rate, and future outlook.

General information This simulation data belongs to the article: Retrieving pulsatility in ultrasound localization microscopy DOI: 10.1109/OJUFFC.2022.3221354 This information is also available in README.txt, included in this repository. Code availability The scripts that should be used to process this data can be found at: https://github.com/qnano/ulm-pulsatility Data description The simulation data in this repository, is contained in several .zip files:

DataSetFig3.zip: This dataset contains the simulation data needed to reproduce Fig. 3 of the article. DataSetR1.zip: This dataset contains the simulation data corresponding to the lateral vessel location R1. It is needed to reproduce Fig 4 and Fig. 6b. DataSetR2.zip: This dataset contains the simulation data corresponding to the lateral vessel location R2. It is needed to reproduce Fig 4 and Fig. 6b. DataSetR3.zip: This dataset contains the simulation data corresponding to the lateral vessel location R3. It is needed to reproduce Fig 4 and Fig. 6b.

The .zip folders contain the following:

LOTUS_INPUT: This folder contains the input simulated ultrasound images, of which the file names are parametrized as:

chunk***.mat: Data set containing IQ variable of size 180x240x1000, containing 1000 B-mode images of dimension 9mmx12mm

ULM_results: This folder is initially empty. Results of running the scripts will be stored in this folder. PAR.mat: Parameters with which the ultrasound images are simulated. See scripts for further details. TOTAL_MB.mat: Contains the variable MB_loc_conc that describes the simulated (ground truth) location of the MBs at each frame (z, x) [mm]. Saved MB tracks used for visualization. One of the following options:

RAW_tracks.mat Track_r1_illustration.mat Track_r2_illustration.mat Track_r3_illustration.mat

GT_theta.mat: Ground truth orientation of the simulated vessels velAv.mat: Average velocity in the simulated vessels during the simulation.

Download the desired .zip file and see the documentation at https://github.com/qnano/ulm-pulsatility for instructions on processing the data.

Access the summary of the Automated Breast Ultrasound market report, featuring key insights, executive summary, market size, CAGR, growth rate, and future outlook.

Access the summary of the 3D Ultrasound market report, featuring key insights, executive summary, market size, CAGR, growth rate, and future outlook.

A large dataset of routinely acquired maternal-fetal screening ultrasound images collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician. Images are divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother’s cervix (widely used for prematurity screening) and a general category to include any other less common image plane. Fetal brain images are further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. Meta information (patient number, us machine, operator) is also provided, as well as the training-test split used in the Nature Sci Rep paper.

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