David N. Roundhill

Evaluation of stroke volume and ventricular mass in a fetal heart model: a novel four-dimensional echocardiographic analysis.

This study aimed to assess the feasibility and accuracy of nongated four‐dimensional echocardiography (4DE) for determining left ventricular (LV) stroke volume (SV) and mass in a fetal heart‐sized LV model.

Volumetric Segmentation of Key Fetal Brain Structures in 3D Ultrasound

Neurosonography is the most widely used imaging technique for assessing neuro-development of the growing fetus in clinical practice. 3D neurosonography has an advantage of quick acquisition but is yet to demonstrate improvements in clinical workflow. In this paper we propose an automatic technique to segment four important fetal brain structures in 3D ultrasound. The technique is built within a Random Decision Forests framework. Our solution includes novel pre-processing and new features. The pre-processing step makes sure that all volumes are in the same coordinate. The new features constrain the appearance framework by adding a novel distance feature. Validation on 51 3D fetal neurosonography images shows that the proposed technique is capable of segmenting fetal brain structures and providing promising qualitative and quantitative results.

Abdomen segmentation in 3D fetal ultrasound using CNN-powered deformable models

In this paper, voxel probability maps generated by a novel fovea fully convolutional network architecture (FovFCN) are used as additional feature images in the context of a segmentation approach based on deformable shape models. The method is applied to fetal 3D ultrasound image data aiming at a segmentation of the abdominal outline of the fetal torso. This is of interest, e.g., for measuring the fetal abdominal circumference, a standard biometric measure in prenatal screening. The method is trained on 126 3D ultrasound images and tested on 30 additional scans. The results show that the approach can successfully combine the advantages of FovFCNs and deformable shape models in the context of challenging image data, such as given by fetal ultrasound. With a mean error of 2.24 mm, the combination of model-based segmentation and neural networks outperforms the separate approaches.

Learning from redundant but inconsistent reference data: anatomical views and measurements for fetal brain screening

In a fetal brain screening examination, a standardized set of anatomical views is inspected and certain biometric measurements are taken in these views. Acquisition of recommended planes requires a certain level of operator expertise. 3D ultrasound has the potential to reduce the manual task to only capture a volume containing the head and to subsequently determine the standard 2D views and measurements automatically. For this purpose, a segmentation model of the fetal brain was created and trained with expert annotations. It was found that the annotations show a considerable intra- and inter-observer variability. To handle the variability, we propose a method to train the model with redundant but inconsistent reference data from many expert users. If the outlier-cleaned average of all reference annotations is considered as ground truth, errors of the automatic view detection are lower than the errors of all individual users and errors of the measurements are in the same range as user error. The resulting functionality allows the completely automated estimation of views and measurements in 3D fetal ultrasound images.

Tissue harmonic imaging in cardiology and radiology applications

Harmonic imaging, the formation of ultrasound images from the harmonic components of the sonification signal, has been used in diagnostic ultrasound applications that utilize microbubble contrast agents. In the presence of microbubbles the harmonic signals are mainly due to nonlinear bubble oscillations. Recently, harmonic imaging has been applied without contrast agents, the harmonic signals arising from nonlinear propagation. Near‐field artifacts and aberrations are reduced, and tissue borders are enhanced. The harmonic signals are at a lower amplitude and suffer more attenuation than the fundamental. However, the wide dynamic range, digital architecture, and signal processing capabilities of modern diagnostic ultrasound systems make it possible to utilize this tissue‐generated harmonic energy for image formation. The nonlinear parabolic wave equation is used to model tissue propagation. A time domain numerical solution for a nonaxisymmetric source function is presented. Theory and experiments demonstra...

Automated abdominal plane and circumference estimation in 3D US for fetal screening.

Ultrasound is increasingly becoming a 3D modality. Mechanical and matrix array transducers are able to deliver 3D images with good spatial and temporal resolution. The 3D imaging facilitates the application of automated image analysis to enhance workflows, which has the potential to make ultrasound a less operator dependent modality. However, the analysis of the more complex 3D images and definition of all examination standards on 2D images pose barriers to the use of 3D in daily clinical practice. In this paper, we address a part of the canonical fetal screening program, namely the localization of the abdominal cross-sectional plane with the corresponding measurement of the abdominal circumference in this plane. For this purpose, a fully automated pipeline has been designed starting with a random forest based anatomical landmark detection. A feature trained shape model of the fetal torso including inner organs with the abdominal cross-sectional plane encoded into the model is then transformed into the patient space using the landmark localizations. In a free-form deformation step, the model is individualized to the image, using a torso probability map generated by a convolutional neural network as an additional feature image. After adaptation, the abdominal plane and the abdominal torso contour in that plane are directly obtained. This allows the measurement of the abdominal circumference as well as the rendering of the plane for visual assessment. The method has been trained on 126 and evaluated on 42 abdominal 3D US datasets. An average plane offset error of 5.8 mm and an average relative circumference error of 4.9 % in the evaluation set could be achieved.

Adaptation of an articulated fetal skeleton model to three-dimensional fetal image data

The automatic interpretation of three-dimensional fetal images poses specific challenges compared to other three-dimensional diagnostic data, especially since the orientation of the fetus in the uterus and the position of the extremities is highly variable. In this paper, we present a comprehensive articulated model of the fetal skeleton and the adaptation of the articulation for pose estimation in three-dimensional fetal images. The model is composed out of rigid bodies where the articulations are represented as rigid body transformations. Given a set of target landmarks, the model constellation can be estimated by optimization of the pose parameters. Experiments are carried out on 3D fetal MRI data yielding an average error per case of 12.03±3.36 mm between target and estimated landmark positions.

System and method for three dimensional harmonic ultrasound imaging

An ultrasonic diagnostic imaging system and method are described which produce tissue harmonic images containing both fundamental and harmonic frequency components. Such a blended image takes advantage of the performance possible with the two types of ultrasonic echo information and can advantageously reduce near field clutter while improving signal to noise performance in the far field of the image.