Intensity-Based Visual Servoing for Instrument and Tissue Tracking in 3D Ultrasound Volumes

Nadeau, Caroline; Ren, Hongliang; Krupa, Alexandre; Dupont, Pierre E.

doi:10.1109/tase.2014.2343652

Cited by 69 publications

(45 citation statements)

References 19 publications

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“…Because the developed control schemes can broadly be translated to robotically steered US probes, a full automation of dedicated parts of surgeries comes into reach, for instance by simultaneous tracking of both anatomy and instruments [24].…”

Section: Related Workmentioning

confidence: 99%

3D ultrasound registration-based visual servoing for neurosurgical navigation

et al. 2017

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Purpose We present a fully image-based visual servoing framework for neurosurgical navigation and needle guidance. The proposed servo-control scheme allows for compensation of target anatomy movements, maintaining high navigational accuracy over time, and automatic needle guide alignment for accurate manual insertions. Method Our system comprises a motorized 3D ultrasound (US) transducer mounted on a robotic arm and equipped with a needle guide. It continuously registers US sweeps in real time with a pre-interventional plan based on CT or MR images and annotations. While a visual control law maintains anatomy visibility and alignment of the needle guide, a force controller is employed for acoustic coupling and tissue pressure. We validate the servoing capabilities of our method on a geometric gel phantom and real human anatomy, and the needle targeting accuracy using CT images on a lumbar spine gel phantom under neurosurgery conditions. Results Despite the varying resolution of the acquired Oliver Zettinig and Benjamin Frisch have contributed equally to this work. Yu-Mi Ryang and Nassir Navab have contributed equally to this work.B Oliver Zettinig 3D sweeps, we achieved direction-independent positioning errors of 0.35±0.19 mm and 0.61 • ±0.45 • , respectively. Our method is capable of compensating movements of around 25 mm/s and works reliably on human anatomy with errors of 1.45 ± 0.78 mm. In all four manual insertions by an expert surgeon, a needle could be successfully inserted into the facet joint, with an estimated targeting accuracy of 1.33±0.33 mm, superior to the gold standard. Conclusion The experiments demonstrated the feasibility of robotic ultrasound-based navigation and needle guidance for neurosurgical applications such as lumbar spine injections.

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Section: Related Workmentioning

confidence: 99%

3D ultrasound registration-based visual servoing for neurosurgical navigation

et al. 2017

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“…To improve the quality of the intervention and to help the clinician to focus on the procedure itself rather than finding the instruments, automatic catheter segmentation in 3-D US images becomes beneficial, because it makes it easier to better identify the catheter in the correct heart chamber in the US images. Many researchers have concentrated on catheter identification in US imaging with the aid of robots, 1 or by adding active sensors inside the catheter. 2 Although these approaches have achieved attractive results, the high cost of equipment and complicated system set up in the operation room have hampered their broad acceptance.…”

Section: Introductionmentioning

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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“…Techniques like active sensing [1] or robotics [2], have introduced extra cost for equipment and training. Alternatively, direct image-based catheter detection will lead to a lower cost for hospitals.…”

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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Intensity-Based Visual Servoing for Instrument and Tissue Tracking in 3D Ultrasound Volumes

Cited by 69 publications

References 19 publications

3D ultrasound registration-based visual servoing for neurosurgical navigation

3D ultrasound registration-based visual servoing for neurosurgical navigation

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

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