Key result
A novel real-time automatic artery segmentation and tracking algorithm successfully initialized in all 48 ultrasound sequences, achieving an average dice similarity coefficient of 0.91.
A novel real-time automatic algorithm successfully segments, tracks, and reconstructs the femoral artery from ultrasound images, providing a foundational tool for ultrasound-guided regional anesthesia.
May aid real-time ultrasound guidance in anesthesia; hypothesis-generating pending clinical validation.
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.
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Smistad et al. (2015) studied Ultrasound-guided femoral nerve block. Real-time automatic artery segmentation, reconstruction and registration algorithm was evaluated on Artery detection, tracking accuracy, and registration error. A novel real-time automatic artery segmentation and tracking algorithm successfully initialized in all 48 ultrasound sequences, achieving an average dice similarity coefficient of 0.91.
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