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A Learning-Based Approach for Fast and Robust Vessel Tracking in Long Ultrasound Sequences
Valeria De Luca, Michael Tschannen, Gábor Székely, and Christine Tanner
Computer Vision Laboratory, ETH Zürich, 8092, Zürich, SwitzerlandAbstract. 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).
Keywords: tracking, block-matching, learning, real-time, ultrasound
LNCS 8149, p. 518 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013