Line filtering for detection of microtools in 3D ultrasound data

We propose a robust method for localization of elongated surgical tools in 3D ultrasound data based on shape analysis. The tubular structures in input data are enhanced by a line filter in the pre-processing phase. A new model of a surgical tool appearance in 3D ultrasound image is proposed which exploits its tubular shape. The tool axis is estimated with robust model fitting using a randomized RANSAC procedure. The tool model requires the voxels close to the axis to have a high intensity, high tubularness, and the local principal directions to be consistent with the tool axis. The visual contrast of the tool can be enhanced four-fold using line filtering. We demonstrate that classification rate is improved by 25–40% when adding the tubularness attribute. The comparison to other state-of-the-art localization methods shows that the proposed method is the most robust for data with high level of noise at the expense of additional time for pre-processing (less than 10 seconds for volume of size 53×71×260 voxels).