Biology subjects

van Sloun, R. J.

Publications and source records attributed to van Sloun, R. J..

2 recordsLinked to original sources

bioRxiv2022

Automated segmentation of fetal intracranial volume in 3D ultrasound using deep learning: identifying sex differences in prenatal brain development

The human brain undergoes major developmental changes during pregnancy. Three-dimensional (3D) ultrasound images allow for the opportunity to investigate typical prenatal brain development on a large scale. Here, we developed a convolutional neural network (CNN) model for automated segmentation of fetal intracranial volume (ICV) from 3D ultrasound, and we applied the trained model in a large independent sample (N = 9795 ultrasounds; N=1763 participants) from the YOUth Baby and Child cohort measured at 20- and 30-week of gestational age to investigate sex differences in fetal ICV and ICV growth. 168 3D ultrasound images with ICV annotations were included to develop a 3D CNN model for automated fetal ICV segmentation. A data augmentation strategy provided physical variation and additional data to train the network. K-fold cross-validation and Bayesian optimization were used for network selection and the ensemble-based system combined multiple networks to form the final ensemble network. The final ensemble network produced consistent and high-quality segmentations of ICV. Our trained model successfully predicted ICV and ICV growth in a large independent longitudinal data set. Boys had significantly larger ICV at 20- (B=2.83; p=1.4e-13) and 30-weeks of pregnancy (B=12.01; p=2.0e-28), and more pronounced ICV growth than girls (t=-4.395; p=1.3e-5). Our approach provides us with the opportunity to investigate fetal brain development on a much larger scale and to answer fundamental questions related to prenatal brain development.

neuroscience↗

bioRxiv2018

Model-Aware Deep Learning Based Strategies for Clutter Suppression in Contrast Enhanced Ultrasound

Contrast enhanced ultrasound is a radiation-free imaging modality which uses encapsulated gas microbubbles for improved visualization of the vascular bed deep within the tissue. It has recently been used to enable imaging with unprecedented subwavelength spatial resolution by relying on super-resolution techniques. A typical preprocessing step in super-resolution ultrasound is to separate the microbubble signal from the cluttering tissue signal. This step has a crucial impact on the final image quality. Here, we propose a new approach to clutter removal based on robust principle component analysis (PCA) and deep learning. We begin by modeling the acquired contrast enhanced ultrasound signal as a combination of a low rank and sparse components. This model is used in robust PCA and was previously suggested in the context of ultrasound Doppler processing and dynamic magnetic resonance imaging. We then illustrate that an iterative algorithm based on this model exhibits improved separation of microbubble signal from the tissue signal over commonly practiced methods. Next, we apply the concept of deep unfolding to suggest a deep network architecture tailored to our clutter filtering problem which exhibits improved convergence speed and accuracy with respect to its iterative counterpart. We compare the performance of the suggested deep network on both simulations and in-vivo rat brain scans, with a commonly practiced deep-network architecture and the fast iterative shrinkage algorithm, and show that our architecture exhibits better image quality and contrast.

bioengineering↗