Ultrasound-based tumor movement compensation during navigated laparoscopic liver interventions
Image-guided navigation aims to provide better orientation and accuracy in laparoscopic interventions. However, the ability of the navigation system to reflect anatomical changes and maintain high accuracy during the procedure is crucial. This is particularly challenging in soft organs such as the liver, where surgical manipulation causes significant tumor movements. We propose a fast approach to obtain an accurate estimation of the tumor position throughout the procedure.
Initially, a three-dimensional (3D) ultrasound image is reconstructed and the tumor is segmented. During surgery, the position of the tumor is updated based on newly acquired tracked ultrasound images. The initial segmentation of the tumor is used to automatically detect the tumor and update its position in the navigation system. Two experiments were conducted. First, a controlled phantom motion using a robot was performed to validate the tracking accuracy. Second, a needle navigation scenario based on pseudotumors injected into ex vivo porcine liver was studied.
In the robot-based evaluation, the approach estimated the target location with an accuracy of 0.4 ± 0.3 mm. The mean navigation error in the needle experiment was 1.2 ± 0.6 mm, and the algorithm compensated for tumor shifts up to 38 mm in an average time of 1 s.
We demonstrated a navigation approach based on tracked laparoscopic ultrasound (LUS), and focused on the neighborhood of the tumor. Our experimental results indicate that this approach can be used to quickly and accurately compensate for tumor movements caused by surgical manipulation during laparoscopic interventions. The proposed approach has the advantage of being based on the routinely used LUS; however, it upgrades its functionality to estimate the tumor position in 3D. Hence, the approach is repeatable throughout surgery, and enables high navigation accuracy to be maintained.
KeywordsLiver Tumor shift Intraoperative ultrasound Tracking Navigation
This work was supported by the Graduate School for Computing in Medicine and Life Sciences funded by Germany’s Excellence Initiative (DFG GSC 235/1).
Osama Shahin, Armin Beširević, Markus Kleemann, and Alexander Schlaefer have no conflicts of interest or financial ties to disclose.
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