  Ultrasound imaging is one of the most prominent technologies to evaluate the
growth, progression, and overall health of a fetus during its gestation.
However, the interpretation of the data obtained from such studies is best left
to expert physicians and technicians who are trained and well-versed in
analyzing such images. To improve the clinical workflow and potentially develop
an at-home ultrasound-based fetal monitoring platform, we present a novel fetus
phantom ultrasound dataset, FPUS23, which can be used to identify (1) the
correct diagnostic planes for estimating fetal biometric values, (2) fetus
orientation, (3) their anatomical features, and (4) bounding boxes of the fetus
phantom anatomies at 23 weeks gestation. The entire dataset is composed of
15,728 images, which are used to train four different Deep Neural Network
models, built upon a ResNet34 backbone, for detecting aforementioned fetus
features and use-cases. We have also evaluated the models trained using our
FPUS23 dataset, to show that the information learned by these models can be
used to substantially increase the accuracy on real-world ultrasound fetus
datasets. We make the FPUS23 dataset and the pre-trained models publicly
accessible at https://github.com/bharathprabakaran/FPUS23, which will further
facilitate future research on fetal ultrasound imaging and analysis.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning