Benchmarking of state-of-the-art needle detection algorithms in 3D ultrasound data volumes

Arash Pourtaherian; Svitlana Zinger; Peter H. N. de With; Hendrikus H. M. Korsten; Nenad Mihajlovic

doi:10.1117/12.2081800

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18 March 2015 Benchmarking of state-of-the-art needle detection algorithms in 3D ultrasound data volumes

Arash Pourtaherian, Svitlana Zinger, Peter H. N. de With, Hendrikus H. M. Korsten, Nenad Mihajlovic

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Proceedings Volume 9415, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling; 94152B (2015) https://doi.org/10.1117/12.2081800
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Ultrasound-guided needle interventions are widely practiced in medical diagnostics and therapy, i.e. for biopsy guidance, regional anesthesia or for brachytherapy. Needle guidance using 2D ultrasound can be very challenging due to the poor needle visibility and the limited field of view. Since 3D ultrasound transducers are becoming more widely used, needle guidance can be improved and simplified with appropriate computer-aided analyses. In this paper, we compare two state-of-the-art 3D needle detection techniques: a technique based on line filtering from literature and a system employing Gabor transformation. Both algorithms utilize supervised classification to pre-select candidate needle voxels in the volume and then fit a model of the needle on the selected voxels. The major differences between the two approaches are in extracting the feature vectors for classification and selecting the criterion for fitting. We evaluate the performance of the two techniques using manually-annotated ground truth in several ex-vivo situations of different complexities, containing three different needle types with various insertion angles. This extensive evaluation provides better understanding on the limitations and advantages of each technique under different acquisition conditions, which is leading to the development of improved techniques for more reliable and accurate localization. Benchmarking results that the Gabor features are better capable of distinguishing the needle voxels in all datasets. Moreover, it is shown that the complete processing chain of the Gabor-based method outperforms the line filtering in accuracy and stability of the detection results.

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Arash Pourtaherian, Svitlana Zinger, Peter H. N. de With, Hendrikus H. M. Korsten, and Nenad Mihajlovic "Benchmarking of state-of-the-art needle detection algorithms in 3D ultrasound data volumes", Proc. SPIE 9415, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling, 94152B (18 March 2015); https://doi.org/10.1117/12.2081800

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