# The Open Kidney Ultrasound Data Set

arXiv: https://arxiv.org/abs/2206.06657 · HTML: https://mlsanity.com/paper/2206.06657/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [Causality matters in medical imaging](https://mlsanity.com/paper/1912.08142/) (2019)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [Common Limitations of Image Processing Metrics: A Picture Story](https://mlsanity.com/paper/2104.05642/) (2021)
- [BIAS: Transparent reporting of biomedical image analysis challenges](https://mlsanity.com/paper/1910.04071/) (2019)
- [Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects](https://mlsanity.com/paper/1910.04597/) (2019)
- [Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks](https://mlsanity.com/paper/1811.04815/) (2018)

## Built on by (most cited first, 18 of 18)

- [UltraSam: A Foundation Model for Ultrasound using Large Open-Access Segmentation Datasets](https://mlsanity.com/paper/2411.16222/) (2024)
- [USF-MAE: Ultrasound Self-Supervised Foundation Model with Masked Autoencoding](https://mlsanity.com/paper/2510.22990/) (2025)
- [U2-BENCH: Benchmarking Large Vision-Language Models on Ultrasound Understanding](https://mlsanity.com/paper/2505.17779/) (2025)
- [Revisiting Data Augmentation for Ultrasound Images](https://mlsanity.com/paper/2501.13193/) (2025)
- [CACTUS: An Open Dataset and Framework for Automated Cardiac Assessment and Classification of Ultrasound Images Using Deep Transfer Learning](https://mlsanity.com/paper/2503.05604/) (2025)
- [TRUSTED: The Paired 3D Transabdominal Ultrasound and CT Human Data for Kidney Segmentation and Registration Research](https://mlsanity.com/paper/2310.12646/) (2023)
- [UniUSNet: A Promptable Framework for Universal Ultrasound Disease Prediction and Tissue Segmentation](https://mlsanity.com/paper/2406.01154/) (2024)
- [Grounding DINO-US-SAM: Text-Prompted Multi-Organ Segmentation in Ultrasound with LoRA-Tuned Vision-Language Models](https://mlsanity.com/paper/2506.23903/) (2025)
- [UMind-VL: A Generalist Ultrasound Vision-Language Model for Unified Grounded Perception and Comprehensive Interpretation](https://mlsanity.com/paper/2511.22256/) (2025)
- [US-JEPA: A Joint Embedding Predictive Architecture for Ultrasound](https://mlsanity.com/paper/2602.19322/) (2026)
- [Task-Specific Knowledge Distillation from the Vision Foundation Model for Enhanced Medical Image Segmentation](https://mlsanity.com/paper/2503.06976/) (2025)
- [A texture-based framework for foundational ultrasound models](https://mlsanity.com/paper/2602.01444/) (2026)
- [UltraSAM3: A Concept-Driven Foundation Model for Universal Ultrasound Image Segmentation](https://mlsanity.com/paper/2607.29200/) (2026)
- [UltraG-Bench: A Multi-task Benchmark for assessing Large Vision-Language Models on Pixel-level Evidence Grounding in Ultrasound](https://mlsanity.com/paper/2609.30928/) (2026)
- [Deep Learning-Based Semantic Segmentation for Real-Time Kidney Imaging and Measurements with Augmented Reality-Assisted Ultrasound](https://mlsanity.com/paper/2506.23721/) (2025)
- [UltraUPConvNet: A UPerNet- and ConvNeXt-Based Multi-Task Network for Ultrasound Tissue Segmentation and Disease Prediction](https://mlsanity.com/paper/2509.11108/) (2025)
- [Diagnostic Performance of Universal-Learning Ultrasound AI Across Multiple Organs and Tasks: the UUSIC25 Challenge](https://mlsanity.com/paper/2512.17279/) (2025)
- [UltraBench 2: Towards Robust Evaluation of Vision Foundation Models on Ultrasound](https://mlsanity.com/paper/2609.28610/) (2026)

## Abstract (arXiv)

Ultrasound, because of its low cost, non-ionizing, and non-invasive characteristics, has established itself as a cornerstone radiological examination. Research on ultrasound applications has also expanded, especially with image analysis with machine learning. However, ultrasound data are frequently restricted to closed data sets, with only a few openly available. Despite being a frequently examined organ, the kidney lacks a publicly available ultrasonography data set. The proposed Open Kidney Ultrasound Data Set is the first publicly available set of kidney brightness mode (B-mode) ultrasound data that includes annotations for multi-class semantic segmentation. It is based on data retrospectively collected in a 5-year period from over 500 patients with a mean age of 53.2 +/- 14.7 years, body mass index of 27.0 +/- 5.4 kg/m2, and most common primary diseases being diabetes mellitus, immunoglobulin A (IgA) nephropathy, and hypertension. There are labels for the view and fine-grained manual annotations from two expert sonographers. Notably, this data includes native and transplanted kidneys. Initial bench-marking measurements are performed, demonstrating a state-of-the-art algorithm achieving a Dice Sorenson Coefficient of 0.85 for the kidney capsule. This data set is a high-quality data set, including two sets of expert annotations, with a larger breadth of images than previously available. In increasing access to kidney ultrasound data, future researchers may be able to create novel image analysis techniques for tissue characterization, disease detection, and prognostication.