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Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from three experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts' measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts' variability range. The platform remains open for new submissions.

The goal is to create an assistant for ultrasound- guided femoral nerve block. By segmenting and visualizing the important structures such as the femoral artery, we hope to improve the success of these procedures. This article is the first step towards this goal and presents novel real-time methods for identifying and reconstructing the femoral artery, and registering a model of the surrounding anatomy to the ultrasound images. The femoral artery is modelled as an ellipse. The artery is first detected by a novel algorithm which initializes the artery tracking. This algorithm is completely automatic and requires no user interaction. Artery tracking is achieved with a Kalman filter. The 3D artery is reconstructed in real-time with a novel algorithm and a tracked ultrasound probe. A mesh model of the surrounding anatomy was created from a CT dataset. Registration of this model is achieved by landmark registration using the centerpoints from the artery tracking and the femoral artery centerline of the model. The artery detection method was able to automatically detect the femoral artery and initialize the tracking in all 48 ultrasound sequences. The tracking algorithm achieved an average dice similarity coefficient of 0.91, absolute distance of 0.33 mm, and Hausdorff distance 1.05 mm. The mean registration error was 2.7 mm, while the average maximum error was 12.4 mm. The average runtime was measured to be 38, 8, 46 and 0.2 milliseconds for the artery detection, tracking, reconstruction and registration methods respectively.

In neurosurgery, information of blood flow is important to identify and avoid damage to important vessels. Three-dimensional intraoperative ultrasound color-Doppler imaging has proven useful in this respect. However, due to Doppler angle-dependencies and the complexity of the vascular architecture, clinical valuable 3-D information of flow direction and velocity is currently not available. In this work, we aim to correct for angle-dependencies in 3-D flow images based on a geometric model of the neurovascular tree generated on-the-fly from free-hand 2-D imaging and an accurate position sensor system. The 3-D vessel model acts as a priori information of vessel orientation used to angle-correct the Doppler measurements, as well as provide an estimate of the average flow direction. Based on the flow direction we were also able to do aliasing correction to approximately double the measurable velocity range. In vitro experiments revealed a high accuracy and robustness for estimating the mean direction of flow. Accurate angle-correction of axial velocities were possible given a sufficient beam-to-flow angle for at least parts of a vessel segment . In vitro experiments showed an absolute relative bias of 9.5% for a challenging low-flow scenario. The method also showed promising results in vivo, improving the depiction of flow in the distal branches of intracranial aneurysms and the feeding arteries of an arteriovenous malformation. Careful inspection by an experienced surgeon confirmed the correct flow direction for all in vivo examples.