Medical Instrument Detection in 3-Dimensional Ultrasound Data Volumes

Abstract

Abstract-Ultrasound-guided medical interventions are broadly applied in diagnostics and therapy, e.g. regional anesthesia or ablation. A guided intervention using 2D ultrasound is challenging due to the poor instrument visibility, limited field of view and the multi-fold coordination of the medical instrument and ultrasound plane. Recent 3D ultrasound transducers can improve the quality of the image-guided intervention if an automated detection of the needle is used. In this paper, we present a novel method for detecting medical instruments in 3D ultrasound data that is solely based on image processing techniques and validated on various ex-vivo and in-vivo datasets. In the proposed procedure, the physician is placing the 3D transducer at the desired position and the image processing will automatically detect the best instrument view, so that the physician can entirely focus on the intervention. Our method is based on classification of instrument voxels using volumetric structure directions and robust approximation of the primary tool axis. A novel normalization method is proposed for the shape and intensity consistency of instruments to improve the detection. Moreover, a novel 3D Gabor wavelet transformation is introduced and optimally designed for revealing the instrument voxels in the volume, while remaining generic to several medical instruments and transducer types. Experiments on diverse datasets including in-vivo data from patients show that for a given transducer and instrument type, high detection accuracies are achieved with position errors smaller than the instrument diameter in the 0.5 to 1.5 millimeter range on average.

Catheter segmentation in three-dimensional ultrasound images by feature fusion and model fitting

“…The medical instrument model is conventionally reconstructed by fitting its skeleton together with instrument body voxels surrounding it. 7,13 However, this method is not stable and inaccurate when assuming a straight-line model in our challenging and noisy classified images. To segment the curved catheter in 3-D US, we first modified the sparse-plus-dense-RANSAC (SPD-RANSAC) 18 from the high-contrast x-ray image into our 3-D US to reduce the complexity of segmentation.…”

“…Furthermore, both Uherčík et al 8 and Zhao et al 10 only considered a predefined Frangi feature as discriminating information, which is not only less robust to diameter variation but also considers a small amount of information only, i.e., information in ultrasound volume is not fully used for discriminating classification. Recently, Pourtaherian et al [11][12][13] have intensively studied needle detection algorithms based on the 3-D US. Their method segments the candidate needle-like voxels by incorporating the Gabor-based feature.…”

“…Although the methods discussed above have shown successful results in 3-D ultrasound-based instrument segmentation, there are still many challenges concerning cardiac intervention. First of all, the spatial resolution of the transducer in cardiac intervention is far lower than the standard one used in the anesthesia (response frequency from 5 to 13 MHz in anesthesia probe 13 to 2 to 7 MHz in the cardiac probe). This low spatial resolution leads to lower image contrast and fewer details at instrument boundaries.…”

“…8,10 In addition, instead of only describing the tubular structure as the traditional Frangi filter, a multidimensional objectness feature is derived. 15 Considering the information loss in objectness features and inspired by the circular shifting in the existing methods, 13 we propose Hessian-based features to fully describe the 3-D information. Next to this, log-Gabor filters are considered to add more orientation information.…”

“…xx ; f σ xy ; f σ xz ; f σ yz ; f σ zz ; f σ yy : (4) Pourtaherian et al [11][12][13] introduced Gabor features as an attractive discriminative feature for needle detection. The conventional Gabor-based features can be influenced by the DC components of the images.…”