Kidney Stone Ultrasound

## Overview

Kidney Stone Ultrasound is a dataset for object detection tasks - it contains Stone annotations for 5,431 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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. | Subject area | Medicine and Dentistry | |——————————————|———————&m

🤰 Ultrasound Fetus Dataset 👶

This dataset, the Ultrasound Fetus Dataset, contains a collection of medical ultrasound scans focused on fetus data. It is designed to support research and development in applying deep learning techniques to the analysis of fetal ultrasound images. 🤰

📄 Description

This dataset is based on medical ultrasound scans displaying information about fetuses. It includes three categories of images:

• Normal: Ultrasound images showing healthy fetuses. ✅
• Benign: Ultrasound images showing fetuses with non-cancerous findings. benign 😊
• Malignant: Ultrasound images showing fetuses with potentially cancerous findings. ⚠️

The dataset is intended to be used with deep learning models for tasks such as:

• Fetus disease level detection: Identifying the presence and severity of fetal conditions. 🔍
• Segmentation: Precisely outlining specific regions of interest in the ultrasound images. 🖍️
• Classification: Categorizing ultrasound images into the normal, benign, or malignant categories. 🏷️

🔑 Categories

Fetus, Ultrasound, Deep Learning

📜 Licence

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt this material for any purpose, even commercially, as long as you give appropriate credit. 📄

✍️ Citation

Please cite this dataset as follows:

Anitha, A (2024), “Ultrasound Fetus Dataset”, Mendeley Data, V1, doi: 10.17632/yrzzw9m6kk.1

626 additional images with thyroid/nodule masks.

Fetal Brain Abnormalities Ultrasound

## Overview

Fetal Brain Abnormalities Ultrasound is a dataset for classification tasks - it contains Normal Abnormal annotations for 1,768 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Fetal Brain Ultrasound

## Overview

Fetal Brain Ultrasound is a dataset for object detection tasks - it contains CSP annotations for 2,740 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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.

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.

Kidney Ultrasound

## Overview

Kidney Ultrasound is a dataset for object detection tasks - it contains Kidney UltraSound annotations for 356 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The MMOTU dataset consists of ovarian ultrasound images collected from Beijing Shijitan Hospital, Capital Medical University. The dataset is divided into two subsets: OTU 2D and OTU CEUS. The OTU 2D subset contains ultrasound images.The OTU CEUS subset consists of 170 images extracted from CEUS sequences.The MMOTU ovarian tumor ultrasound dataset used in the paper titled "PMFFNet: A hybrid network based on feature pyramid for ovarian tumor segmentation" is stored here. If needed, you can download and access it yourself. The dataset we employed in our study is sourced from the MMOTU image dataset, which comprises ovarian ultrasound images collected from Beijing Shijitan Hospital, Capital Medical University.If you would like to access the original MMOTU dataset, please click on the following link: https://drive.google.com/drive/folders/1c5n0fVKrM9-SZE1kacTXPt1pt844iAs1

EchoNet-Dynamic is a dataset of over 10k echocardiogram, or cardiac ultrasound, videos from unique patients at Stanford University Medical Center. Each apical-4-chamber video is accompanied by an estimated ejection fraction, end-systolic volume, end-diastolic volume, and tracings of the left ventricle performed by an advanced cardiac sonographer and reviewed by an imaging cardiologist.

The dataset comprises 9416 images categorized into 'Normal' and 'Stone' with 4414 and 5002 images respectively, collected from various scan centers and hospitals while ensuring the privacy and confidentiality of patient information. These images are obtained by using different ultrasound machines namely: SAMSUNG RS85, SAMSUNG HS60, SAMSUNG RS80A, SAMSUNG HS70A etc.

Musculoskeletal disorders present significant health and economic challenges on a global scale. Current intraoperative imaging techniques, including computed tomography (CT) and radiography, involve high radiation exposure and limited soft tissue visualization. Ultrasound (US) offers a non-invasive, real-time alternative but is highly observer-dependent and underutilized intraoperatively. US enhanced by artificial intelligence shows high potential for observer-independent pattern recognition and robot-assisted applications in orthopedics. Given the limited availability of in-vivo imaging data, we introduce a comprehensive dataset from a comparative collection of handheld US (HUS) and robot-assisted ultrasound (RUS) lumbar spine imaging in 63 healthy volunteers. This dataset includes demographic data, paired CT, HUS, RUS imaging, synchronized tracking data for HUS and RUS, and 3D-CT-segmentations. It establishes a robust baseline for machine learning algorithms by focusing on healthy individuals, circumventing the limitations of simulations and pathological anatomy. To our knowledge, this extensive collection is the first healthy anatomy dataset for the lumbar spine that includes paired CT, HUS, and RUS imaging, supporting advancements in computer- and robotic-assisted diagnostic and intraoperative techniques for musculoskeletal disorders.

Transcranial Doppler (TCD) echo data was recorded from healthy adults and neurocritical care adult patients. The insonated cerebral vessels were the middle cerebral artery (MCA) and the internal carotid artery (ICA). The ultrasound system used in this study was the Philips CX50.

Breast Cancer Ultrasound Images

This dataset has been created by hand for scientific and learning purposes. It is used in "Fine-tuning a Vision Transformer Model With a Custom Biomedical Dataset" in Hugging Face Cookbook.

  Dataset Details

The dataset contains total of 780 images from the following 3 classes: benign, malignant and normal. You can use this dataset for your computer vision tasks.

  Used model

You can use this fine-tuned Vision Transformer Model with… See the full description on the dataset page: https://huggingface.co/datasets/emre570/breastcancer-ultrasound-images.

Ultrasound imaging data (RF and scan-converted formats) of custom-made elastography phantoms. The data are robotically-acquired image sequences with controlled and gradually increasing phantom indentation. The phantom consists of a background medium traversed by a cylindrical inclusion. The data (images and RF signals) are contained in the 6 folders Acqui1 → Acqui6 corresponding to the 6 acquired image sequences on the phantom. Each Acqui_i folder contains two subfolders: the RF folder containing RF signals in .mat file format (readable in Matlab), and the folder US_Image containing the images of the sequence in PNG format. Both images and RF signals are numbered, with each index corresponding to an indentation level and a force measured by the force sensor as outlined in the Excel file (Tabulation 1). Each RFi.mat file comprises 3152 rows representing the signal along the temporal axis, and 256 columns corresponding to the number of A lines in the image. The Excel file has 5 tabs: -The first tab contains, for each of the 6 acquired image sequences, the frame number in the sequence, the corresponding indentation of the probe (in mm), the recorded voltage value on the force sensor (V), and the corresponding calculated force value (N). Thus, each image in the 6 sequences is identified by a frame number, an indentation, and a force value. -The second tab provide the acquisition parameters of the ultrasound images (frequency, depth, gain, etc.) performed using the SonixTablet ultrasound system from Ultrasonix (now bk medical). -The third tab contains stress-strain curves, the mean, and the standard deviation of the Young's modulus for both the inclusion and the background of the phantom. The Young's modulus is obtained through compression tests conducted by an electromechanical testing machine (Bose, Electroforce 3200) on 10 small cylindrical samples taken from the background and the inclusion. -The fourth tab contains the geometry and dimensions of the phantom. -The fifth tab contains the recipe used to make the gelatin phantom.

This dataset was derived from tracked biopsy sessions using the Artemis biopsy system, many of which included image fusion with MRI targets. Patients received a 3D transrectal ultrasound scan, after which nonrigid registration (e.g. “fusion”) was performed between real-time ultrasound and preoperative MRI, enabling biopsy cores to be sampled from MR regions of interest. Most cases also included sampling of systematic biopsy cores using a 12-core digital template. The Artemis system tracked targeted and systematic core locations using encoder kinematics of a mechanical arm, and recorded locations relative to the Ultrasound scan. MRI biopsy coordinates were also recorded for most cases. STL files and biopsy overlays are available and can be visualized in 3D Slicer with the SlicerHeart extension. Spreadsheets summarizing biopsy and MR target data are also available. See the Detailed Description tab below for more information.

MRI targets were defined using multiparametric MRI, e.g. t2-weighted, diffusion-weighted, and perfusion-weighted sequences, and scored on a Likert-like scale with close correspondence to PIRADS version 2. t2-weighted MRI was used to trace ROI contours, and is the only sequence provided in this dataset. MR imaging was performed on a 3 Tesla Trio, Verio or Skyra scanner (Siemens, Erlangen, Germany). A transabdominal phased array was used in all cases, and an endorectal coil was used in a subset of cases. The majority of pulse sequences are 3D T2:SPC, with TR/TE 2200/203, Matrix/FOV 256 × 205/14 × 14 cm, and 1.5mm slice spacing. Some cases were instead 3D T2:TSE with TR/TE 3800–5040/101, and a small minority were imported from other institutions (various T2 protocols.)

Ultrasound scans were performed with Hitachi Hi-Vision 5500 7.5 MHz or the Noblus C41V 2-10 MHz end-fire probe. 3D scans were acquired by rotation of the end-fire probe 200 degrees about its axis, and interpolating to resample the volume with isotropic resolution.

Patients with suspicion of prostate cancer due to elevated PSA and/or suspicious imaging findings were consecutively accrued. Any consented patient who underwent or had planned to receive a routine, standard-of-care prostate biopsy at the UCLA Clark Urology Center was included.

Note: Some Private Tags in this collection are critical to properly displaying the STL surface and the Prostate anatomy. Private Tag (1129,"Eigen, Inc",1016) DS VoxelSize is especially important for multi-frame US cases.

🩺 HiSBreast

Breast Ultrasound Dataset from Vietnam

https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg" alt="License: CC BY 4.0">
License Details

Meet HiSBreast, a dataset of 972 breast ultrasound samples from Ca Mau General Hospital, Vietnam. Packed with images, tumor descriptions, and diagnoses—all in Vietnamese—it’s a goldmine for breast cancer research and AI innovation! 📸✨

📜 What’s This About?

Collected from real patients, HiSBreast offers 972 ultrasound images alongside detailed tumor signs and doctor diagnoses. Organized into four folders (images, JSON data, descriptions, diagnoses), it’s perfect for advancing medical imaging and breast cancer detection. 🩻

📊 What’s Inside?

• Samples: 972
• Folders:
  • json: Base64 images, descriptions, diagnoses, patient codes
  • image: Decoded ultrasound images
  • diagnoses: Doctor’s diagnoses
  • descriptions: Tumor signs & characteristics
• Format: Images (decoded), JSON, text (Vietnamese)
• Size: 1.9 GB (976 MB + 972 MB)

💡 What Can You Do With It?

• Detect & analyze breast tumors with ultrasound. 🔍
• Train AI for cancer diagnosis and classification. 🤖
• Explore tumor characteristics in medical imaging. 📈

📜 Citation

Using HiSBreast? Cite it:

Luong, Huong Hoang; Nguyen Thanh, Hai; Nguyen, Thai-Nghe; Luong Thi Thu, Huong (2024), HiSBreast, Mendeley Data, V2, doi:10.17632/5c723rpwz2.2

🧑‍🤝‍🧑 Contributors

• Huong Hoang Luong
• Hai Nguyen Thanh
• Thai-Nghe Nguyen
• Huong Luong Thi Thu

Institutions: Can Tho University, FPT University

📜 License

Creative Commons Attribution 4.0 (CC BY 4.0).

✅ Share and adapt—just give credit!

🌟 Let’s Make an Impact!

HiSBreast is your key to advancing breast cancer research—download, innovate, and upvote if it inspires you. Let’s improve lives together! 🙌

The dataset contains matlab (.mat) files related to the radiofrequency (RF) ultrasound (US) data from bone-mimicking materials.

The analysed materials were prepared and scanned as described in the methods section of the related article, published on scientific reports journal (A. Sorriento, A. Poliziani, A. Cafarelli, G. Valenza, L. Ricotti, A novel quantitative and reference-free ultrasound analysis to discriminate different concentrations of bone mineral content, Scientific Reports, 2020, in press, doi: https://doi.org/10.1038/s41598-020-79365-0).

In particular, the US acquisitions were performed using an ArtUS EXT-1H system (Telemed, Italy) equipped with a 192 elements linear probe L15-7H40-A5 working in the frequency range 7.5-15 MHz. The RF data at the trasmission frequency of 15 MHz were analysed as described in the article to get the final results. Here, data acquired at each trasmission frequency (7.5 MHz, 10 MHz, 12MHz and 15MHz) are collected.