Feature detection from echocardiography images using local phase information

Ultrasound images are characterized by their special speckle appearance, low contrast, and low signal-to-noise ratio. It is always challenging to extract important clinical information from these images. An important step before formal analysis is to transform the image to significant features of interest. Intensity based methods do not perform particularly well on ultrasound images. However, it has been previously shown that these images respond well to local phase-based methods which are theoretically intensity-invariant and thus suitable for ultrasound images. We extend the previous local phase-based method to detect features using the local phase computed from monogenic signal which is an isotropic extension of the analytic signal. We apply our method of multiscale feature-asymmetry measurement and local phase-gradient computation to cardiac ultrasound (echocardiography) images for the detection of endocardial, epicardial and myocardial centerline

Augmenting CT cardiac roadmaps with segmented streaming ultrasound

Static X-ray computed tomography (CT) volumes are often used as anatomic roadmaps during catheter-based cardiac interventions performed under X-ray fluoroscopy guidance. These CT volumes provide a high-resolution depiction of soft-tissue structures, but at only a single point within the cardiac and respiratory cycles. Augmenting these static CT roadmaps with segmented myocardial borders extracted from live ultrasound (US) provides intra-operative access to real-time dynamic information about the cardiac anatomy. In this work, using a customized segmentation method based on a 3D active mesh, endocardial borders of the left ventricle were extracted from US image streams (4D data sets) at a frame rate of approximately 5 frames per second. The coordinate systems for CT and US modalities were registered using rigid body registration based on manually selected landmarks, and the segmented endocardial surfaces were overlaid onto the CT volume. The root-mean squared fiducial registration error was 3.80 mm. The accuracy of the segmentation was quantitatively evaluated in phantom and human volunteer studies via comparison with manual tracings on 9 randomly selected frames using a finite-element model (the US image resolutions of the phantom and volunteer data were 1.3 x 1.1 x 1.3 mm and 0.70 x 0.82 x 0.77 mm, respectively). This comparison yielded 3.70±2.5 mm (approximately 3 pixels) root-mean squared error (RMSE) in a phantom study and 2.58±1.58 mm (approximately 3 pixels) RMSE in a clinical study. The combination of static anatomical roadmap volumes and dynamic intra-operative anatomic information will enable better guidance and feedback for image-guided minimally invasive cardiac interventions

Bimodal automated carotid ultrasound segmentation using geometrically constrained deep neural networks

For asymptomatic patients suffering from carotid stenosis, the assessment of plaque morphology is an important clinical task which allows monitoring of the risk of plaque rupture and future incidents of stroke. Ultrasound Imaging provides a safe and non-invasive modality for this, and the segmentation of media-adventitia boundaries and lumen-intima boundaries of the Carotid artery form an essential part in this monitoring process. In this paper, we propose a novel Deep Neural Network as a fully automated segmentation tool, and its application in delineating both the media-adventitia boundary and the lumen-intima boundary. We develop a new geometrically constrained objective function as part of the Network's Stochastic Gradient Descent optimisation, thus tuning it to the problem at hand. Furthermore, we also apply a bimodal fusion of amplitude and phase congruency data proposed by us in previous work, as an input to the network, as the latter provides an intensity-invariant data source to the network. We finally report the segmentation performance of the network on transverse sections of the carotid. Tests are carried out on an augmented dataset of 81,000 images, and the results are compared to other studies by reporting the DICE coefficient of similarity, modified Hausdorff Distance, sensitivity and specificity. Our proposed modification is shown to yield improved results on the standard network over this larger dataset, with the advantage of it being fully automated. We conclude that Deep Neural Networks provide a reliable trained manner in which carotid ultrasound images may be automatically segmented, using amplitude data and intensity invariant phase congruency maps as a data source