title: A learning-based approach for fast and robust vessel tracking in long ultrasound sequences.
abstract: We propose a learning-based method for robust tracking in long ultrasound sequences for image guidance applications. The framework is based on a scale-adaptive block-matching and temporal realignment driven by the image appearance learned from an initial training phase. The latter is introduced to avoid error accumulation over long sequences. The vessel tracking performance is assessed on long 2D ultrasound sequences of the liver of 9 volunteers under free breathing. We achieve a mean tracking accuracy of 0.96 mm. Without learning, the error increases significantly (2.19 mm, p<0.001).
authors: Computer Vision Laboratory, ETH Zürich, 8092 Zürich, Switzerland.
authors: Valeria
authors: V
authors: De Luca
authors: Computer Vision Laboratory, ETH Zürich, 8092 Zürich, Switzerland.
authors: Michael
authors: M
authors: Tschannen
authors: Computer Vision Laboratory, ETH Zürich, 8092 Zürich, Switzerland.
authors: Gábor
authors: G
authors: Székely
authors: Computer Vision Laboratory, ETH Zürich, 8092 Zürich, Switzerland.
authors: Christine
authors: C
authors: Tanner
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D064368:Healthy Volunteers; D006499:Hepatic Artery / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000379:methods*