Laparoscopic image-based navigation for microwave ablation of liver tumors-A multi-center study.

BACKGROUND: Stereotactic navigation technology has been proposed to augment accuracy in targeting intrahepatic lesions for local ablation therapy. This retrospective study evaluated accuracy, efficacy, and safety when using laparoscopic image-guided microwave ablation (LIMA) for malignant liver tumors. METHODS: All patients treated for malignant liver lesions using LIMA at two European centers between 2013 and 2015 were included for analysis. A landmark-based registration technique was applied for intraoperative tumor localization and positioning of ablation probes. Intraoperative efficiency of the procedure was measured as number of registration attempts and time needed to achieve sufficient registration accuracy. Technical accuracy was assessed as Fiducial Registration Error (FRE). Outcome at 90 days including mortality, postoperative morbidity, rates of incomplete ablations, and early intrahepatic recurrences were reported. RESULTS: In 34 months, 54 interventions were performed comprising a total of 346 lesions (median lesions per patient 3 (1-25)). Eleven patients had concomitant laparoscopic resections of the liver or the colorectal primary tumor. Median time for registration was 4:38 min (0:26-19:34). Average FRE was 8.1 ± 2.8 mm. Follow-up at 90 days showed one death, 24% grade I/II, and 4% grade IIIa complications. Median length of hospital stay was 2 days (1-11). Early local recurrence was 9% per lesion and 32% per patient. Of these, 63% were successfully re-ablated within 6 months. CONCLUSIONS: LIMA does not interfere with the intraoperative workflow and results in low complication and early local recurrence rates, even when simultaneously targeting multiple lesions. LIMA may represent a valid therapy option for patients with extensive hepatic disease within a multimodal treatment approach.

A Novel Ultrasound-Based Registration for Image-Guided Laparoscopic Liver Ablation.

Background Patient-to-image registration is a core process of image-guided surgery (IGS) systems. We present a novel registration approach for application in laparoscopic liver surgery, which reconstructs in real time an intraoperative volume of the underlying intrahepatic vessels through an ultrasound (US) sweep process. Methods An existing IGS system for an open liver procedure was adapted, with suitable instrument tracking for laparoscopic equipment. Registration accuracy was evaluated on a realistic phantom by computing the target registration error (TRE) for 5 intrahepatic tumors. The registration work flow was evaluated by computing the time required for performing the registration. Additionally, a scheme for intraoperative accuracy assessment by visual overlay of the US image with preoperative image data was evaluated. Results The proposed registration method achieved an average TRE of 7.2 mm in the left lobe and 9.7 mm in the right lobe. The average time required for performing the registration was 12 minutes. A positive correlation was found between the intraoperative accuracy assessment and the obtained TREs. Conclusions The registration accuracy of the proposed method is adequate for laparoscopic intrahepatic tumor targeting. The presented approach is feasible and fast and may, therefore, not be disruptive to the current surgical work flow.

Ultrasound guidance in navigated liver surgery: toward deep-learning enhanced compensation of deformation and organ motion.

PURPOSE: Accuracy of image-guided liver surgery is challenged by deformation of the liver during the procedure. This study aims at improving navigation accuracy by using intraoperative deep learning segmentation and nonrigid registration of hepatic vasculature from ultrasound (US) images to compensate for changes in liver position and deformation. METHODS: This was a single-center prospective study of patients with liver metastases from any origin. Electromagnetic tracking was used to follow US and liver movement. A preoperative 3D model of the liver, including liver lesions, and hepatic and portal vasculature, was registered with the intraoperative organ position. Hepatic vasculature was segmented using a reduced 3D U-Net and registered to preoperative imaging after initial alignment followed by nonrigid registration. Accuracy was assessed as Euclidean distance between the tumor center imaged in the intraoperative US and the registered preoperative image. RESULTS: Median target registration error (TRE) after initial alignment was 11.6 mm in 25 procedures and improved to 6.9 mm after nonrigid registration (p = 0.0076). The number of TREs above 10 mm halved from 16 to 8 after nonrigid registration. In 9 cases, registration was performed twice after failure of the first attempt. The first registration cycle was completed in median 11 min (8:00-18:45 min) and a second in 5 min (2:30-10:20 min). CONCLUSION: This novel registration workflow using automatic vascular detection and nonrigid registration allows to accurately localize liver lesions. Further automation in the workflow is required in initial alignment and classification accuracy.

Automatic hepatic tumor segmentation in intra-operative ultrasound: a supervised deep-learning approach.

Purpose: Training and evaluation of the performance of a supervised deep-learning model for the segmentation of hepatic tumors from intraoperative US (iUS) images, with the purpose of improving the accuracy of tumor margin assessment during liver surgeries and the detection of lesions during colorectal surgeries. Approach: In this retrospective study, a U-Net network was trained with the nnU-Net framework in different configurations for the segmentation of CRLM from iUS. The model was trained on B-mode intraoperative hepatic US images, hand-labeled by an expert clinician. The model was tested on an independent set of similar images. The average age of the study population was 61.9 ± 9.9 years. Ground truth for the test set was provided by a radiologist, and three extra delineation sets were used for the computation of inter-observer variability. Results: The presented model achieved a DSC of 0.84 (p=0.0037), which is comparable to the expert human raters scores. The model segmented hypoechoic and mixed lesions more accurately (DSC of 0.89 and 0.88, respectively) than hyper- and isoechoic ones (DSC of 0.70 and 0.60, respectively) only missing isoechoic or >20 mm in diameter (8% of the tumors) lesions. The inclusion of extra margins of probable tumor tissue around the lesions in the training ground truth resulted in lower DSCs of 0.75 (p=0.0022). Conclusion: The model can accurately segment hepatic tumors from iUS images and has the potential to speed up the resection margin definition during surgeries and the detection of lesion in screenings by automating iUS assessment.

Augmented environments for the targeting of hepatic lesions during image-guided robotic liver surgery.

BACKGROUND: Stereotactic navigation technology can enhance guidance during surgery and enable the precise reproduction of planned surgical strategies. Currently, specific systems (such as the CAS-One system) are available for instrument guidance in open liver surgery. This study aims to evaluate the implementation of such a system for the targeting of hepatic tumors during robotic liver surgery. MATERIAL AND METHODS: Optical tracking references were attached to one of the robotic instruments and to the robotic endoscopic camera. After instrument and video calibration and patient-to-image registration, a virtual model of the tracked instrument and the available three-dimensional images of the liver were displayed directly within the robotic console, superimposed onto the endoscopic video image. An additional superimposed targeting viewer allowed for the visualization of the target tumor, relative to the tip of the instrument, for an assessment of the distance between the tumor and the tool for the realization of safe resection margins. RESULTS: Two cirrhotic patients underwent robotic navigated atypical hepatic resections for hepatocellular carcinoma. The augmented endoscopic view allowed for the definition of an accurate resection margin around the tumor. The overlay of reconstructed three-dimensional models was also used during parenchymal transection for the identification of vascular and biliary structures. Operative times were 240 min in the first case and 300 min in the second. There were no intraoperative complications. CONCLUSIONS: The da Vinci Surgical System provided an excellent platform for image-guided liver surgery with a stable optic and instrumentation. Robotic image guidance might improve the surgeon's orientation during the operation and increase accuracy in tumor resection. Further developments of this technological combination are needed to deal with organ deformation during surgery.

Ultrasound-based navigation for open liver surgery using active liver tracking.

PURPOSE: Despite extensive preoperative imaging, intraoperative localization of liver lesions after systemic treatment can be challenging. Therefore, an image-guided navigation setup is explored that links preoperative diagnostic scans and 3D models to intraoperative ultrasound (US), enabling overlay of detailed diagnostic images on intraoperative US. Aim of this study is to assess the workflow and accuracy of such a navigation system which compensates for liver motion. METHODS: Electromagnetic (EM) tracking was used for organ tracking and movement of the transducer. After laparotomy, a sensor was attached to the liver surface while the EM-tracked US transducer enabled image acquisition and landmark digitization. Landmarks surrounding the lesion were selected during patient-specific preoperative 3D planning and identified for registration during surgery. Endpoints were accuracy and additional times of the investigative steps. Accuracy was computed at the center of the target lesion. RESULTS: In total, 22 navigated procedures were performed. Navigation provided useful visualization of preoperative 3D models and their overlay on US imaging. Landmark-based registration resulted in a mean fiducial registration error of 10.3 ± 4.3 mm, and a mean target registration error of 8.5 ± 4.2 mm. Navigation was available after an average of 12.7 min. CONCLUSION: We developed a navigation method combining ultrasound with active liver tracking for organ motion compensation, with an accuracy below 10 mm. Fixation of the liver sensor near the target lesion compensates for local movement and contributes to improved reliability during navigation. This represents an important step forward in providing surgical navigation throughout the procedure. TRIAL REGISTRATION: This study is registered in the Netherlands Trial Register (number NL7951).

Technical note: Validation of 3D ultrasound for image registration during oncological liver surgery.

PURPOSE: Registration of pre- and intraoperative images is a crucial step of surgical liver navigation, where rigid registration of vessel centerlines is currently commonly used. When using 3D ultrasound (US), accuracy during navigation might be influenced by the size of the intraoperative US volume, yet the relationship between registration accuracy and US volume size is understudied. In this study, we specify an optimal 3D US volume size for registration using varying volumes of liver vasculature. While previous studies measured accuracy at registered fiducials, in this work, accuracy is determined at the target lesion which is clinically the most relevant structure. METHODS: Three-dimensional US volumes were acquired in 14 patients after laparotomy and liver mobilization. Manual segmentation of vasculature and centerline extraction was performed. Intraoperative and preoperative vasculature centerlines were registered with coherent point drift, using different sub-volumes (sphere with radius r = 30, 40, …, 120 mm). Accuracy was measured by fiducial registration error (FRE) between vessel centerlines and target registration error (TRE) at the center of the target lesion. RESULTS: The lowest FRE for vessel registration was reached with r = 50 mm (6.5 ± 2.5 mm), the highest with r = 120 mm (7.1 ± 2.1 mm). Clinical accuracy at the target lesion, resulted most accurate (TRE = 8.8 ± 5.0 mm) in sub-volumes with a radius of 50 mm. Smaller US sub-volumes resulted in lower average TREs when compared to larger US sub-volumes (Pearson's correlation coefficient R = 0.91, p < 0.001). CONCLUSION: Our results indicate that there is a linear correlation between US volume size and registration accuracy at the tumor. Volumes with radii of 50 mm around the target lesion yield higher accuracy (p < 0.05) (Trial number IRBd18032, 11 September 2018).

A clinically applicable laser-based image-guided system for laparoscopic liver procedures.

PURPOSE: Laser range scanners (LRS) allow performing a surface scan without physical contact with the organ, yielding higher registration accuracy for image-guided surgery (IGS) systems. However, the use of LRS-based registration in laparoscopic liver surgery is still limited because current solutions are composed of expensive and bulky equipment which can hardly be integrated in a surgical scenario. METHODS: In this work, we present a novel LRS-based IGS system for laparoscopic liver procedures. A triangulation process is formulated to compute the 3D coordinates of laser points by using the existing IGS system tracking devices. This allows the use of a compact and cost-effective LRS and therefore facilitates the integration into the laparoscopic setup. The 3D laser points are then reconstructed into a surface to register to the preoperative liver model using a multi-level registration process. RESULTS: Experimental results show that the proposed system provides submillimeter scanning precision and accuracy comparable to those reported in the literature. Further quantitative analysis shows that the proposed system is able to achieve a patient-to-image registration accuracy, described as target registration error, of [Formula: see text]. CONCLUSIONS: We believe that the presented approach will lead to a faster integration of LRS-based registration techniques in the surgical environment. Further studies will focus on optimizing scanning time and on the respiratory motion compensation.