Line filtering for surgical tool localization in 3D ultrasound images

Uhercik, Marian; Kybic, Jan; Zhao, Yue; Cachard, Christian; Liebgott, Hervé

doi:10.1016/j.compbiomed.2013.09.020

Cited by 38 publications

(26 citation statements)

References 40 publications

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“…In addition, the carefully designed template is not only unstable to catheter appearance variations but also lacking discriminating information. Alternatively, Uherčík et al 7,8 applied Frangi et al 9 vesselness features to classify instrument's voxels using supervised learning algorithms. The model-fitting based on RANdom SAmple Consensus (RANSAC) was applied to determine straight tubular-like instruments.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2019

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Ultrasound (US) has been increasingly used during interventions, such as cardiac catheterization. To accurately identify the catheter inside US images, extra training for physicians and sonographers is needed. As a consequence, automated segmentation of the catheter in US images and optimized presentation viewing to the physician can be beneficial to accelerate the efficiency and safety of interventions and improve their outcome. For cardiac catheterization, a three-dimensional (3-D) US image is potentially attractive because of no radiation modality and richer spatial information. However, due to a limited spatial resolution of 3-D cardiac US and complex anatomical structures inside the heart, image-based catheter segmentation is challenging. We propose a cardiac catheter segmentation method in 3-D US data through image processing techniques. Our method first applies a voxel-based classification through newly designed multiscale and multidefinition features, which provide a robust catheter voxel segmentation in 3-D US. Second, a modified catheter model fitting is applied to segment the curved catheter in 3-D US images. The proposed method is validated with extensive experiments, using different in-vitro, ex-vivo, and in-vivo datasets. The proposed method can segment the catheter within an average tip-point error that is smaller than the catheter diameter (1.9 mm) in the volumetric images. Based on automated catheter segmentation and combined with optimal viewing, physicians do not have to interpret US images and can focus on the procedure itself to improve the quality of cardiac intervention.

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Section: Related Workmentioning

confidence: 99%

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

et al. 2019

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“…A template matching approach was proposed by Cao [3]. Alternatively, the Frangi feature was introduced with supervised learning methods and shown to give successful detection for instruments [4]. A recent study on instrument detection combined Gabor features with Frangi features for catheter detection in phantom heart data, which showed promising results in Phantom [5].…”

Section: Introductionmentioning

confidence: 99%

Improving Catheter Segmentation & Localization in 3D Cardiac Ultrasound Using Direction-Fused Fcn

Yang

Shan

Kolen

2019

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

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Fast and accurate catheter detection in cardiac catheterization using harmless 3D ultrasound (US) can improve the efficiency and outcome of the intervention. However, the low image quality of US requires extra training for sonographers to localize the catheter. In this paper, we propose a catheter detection method based on a pre-trained VGG network, which exploits 3D information through re-organized cross-sections to segment the catheter by a shared fully convolutional network (FCN), which is called a Direction- Fused FCN (DF-FCN). Based on the segmented image of DF-FCN, the catheter can be localized by model fitting. Our experiments show that the proposed method can successfully detect an ablation catheter in a challenging ex-vivo 3D US dataset, which was collected on the porcine heart. Extensive analysis shows that the proposed method achieves a Dice score of 57.7%, which offers at least an 11.8% improvement when compared to state-ofthe-art instrument detection methods. Due to the improved segmentation performance by the DF-FCN, the catheter can be localized with an error of only 1.4 mm.

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“…We implement and evaluate the performance of our proposed feature vector, F G , the feature vector taken from the the state-of-the-art [30], F L , and feature vectors based on the combinations of the two, F C , and F E . The implemented feature vectors are classified using the LDA and LSVM classifiers, which are simple and suitable for fast analysis of a large set of data points.…”

Section: B Voxel-wise Classificationmentioning

confidence: 99%

“…Descriptors such as Frangi's line filtering [27], Gabor [28], and log-Gabor features [29] are used to create a data representation that is invariant to small changes in brightness and appearance. A combination of intensity and line filtering descriptors is proposed in [30].…”

Section: A Image-based Instrument Detection In Us Datamentioning

confidence: 99%

See 1 more Smart Citation

Medical Instrument Detection in 3-Dimensional Ultrasound Data Volumes

Pourtaherian

Scholten

Kusters

et al. 2017

IEEE Trans. Med. Imaging

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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.

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Line filtering for surgical tool localization in 3D ultrasound images

Cited by 38 publications

References 40 publications

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

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

Improving Catheter Segmentation & Localization in 3D Cardiac Ultrasound Using Direction-Fused Fcn

Medical Instrument Detection in 3-Dimensional Ultrasound Data Volumes

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