  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.
