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Background The laparoscopic approach to liver resection may reduce morbidity and hospital stay. However, uptake has been slow due to concerns about patient safety and oncological radicality. Image guidance systems may improve patient safety by enabling 3D visualisation of critical intra- and extrahepatic structures. Current systems suffer from non-intuitive visualisation and a complicated setup process. A novel image guidance system (SmartLiver), offering augmented reality visualisation and semi-automatic registration has been developed to address these issues. A clinical feasibility study evaluated the performance and usability of SmartLiver with either manual or semi-automatic registration. Methods Intraoperative image guidance data were recorded and analysed in patients undergoing laparoscopic liver resection or cancer staging. Stereoscopic surface reconstruction and iterative closest point matching facilitated semi-automatic registration. The primary endpoint was defined as successful registration as determined by the operating surgeon. Secondary endpoints were system usability as assessed by a surgeon questionnaire and comparison of manual vs. semi-automatic registration accuracy. Since SmartLiver is still in development no attempt was made to evaluate its impact on perioperative outcomes. Results The primary endpoint was achieved in 16 out of 18 patients. Initially semi-automatic registration failed because the IGS could not distinguish the liver surface from surrounding structures. Implementation of a deep learning algorithm enabled the IGS to overcome this issue and facilitate semi-automatic registration. Mean registration accuracy was 10.9 ± 4.2 mm (manual) vs. 13.9 ± 4.4 mm (semi-automatic) (Mean difference − 3 mm; p = 0.158). Surgeon feedback was positive about IGS handling and improved intraoperative orientation but also highlighted the need for a simpler setup process and better integration with laparoscopic ultrasound. Conclusion The technical feasibility of using SmartLiver intraoperatively has been demonstrated. With further improvements semi-automatic registration may enhance user friendliness and workflow of SmartLiver. Manual and semi-automatic registration accuracy were comparable but evaluation on a larger patient cohort is required to confirm these findings.

We present an analysis of the registration component of a proposed image guidance system for image guided liver surgery, using contrast enhanced CT. The analysis is performed on a visually realistic liver phantom and in-vivo porcine data. A robust registration process that can be deployed clinically is a key component of any image guided surgery system. It is also essential that the accuracy of the registration can be quantified and communicated to the surgeon. We summarise the proposed guidance system and discuss its clinical feasibility. The registration combines an intuitive manual alignment stage, surface reconstruction from a tracked stereo laparoscope and a rigid iterative closest point registration to register the intra-operative liver surface to the liver surface derived from CT. Testing of the system on a liver phantom shows that subsurface landmarks can be localised to an accuracy of 2.9 mm RMS. Testing during five porcine liver surgeries demonstrated that registration can be performed during surgery, with an error of less than 10 mm RMS for multiple surface landmarks.

The insufflation of the abdomen in laparoscopic liver surgery leads to significant deformation of the liver. The estimation of the shape and position of the liver after insufflation has many important applications, such as providing surface-based registration algorithms used in image guidance with an initial guess and realistic patient-specific surgical simulation.

Introduction Compared to open surgery, laparoscopic liver resection (LLR) of cancer benefits patients by reducing pain, length of stay and morbidity. However, LLR is often more challenging than open surgery due to the difficulty of identifying and dividing major vascular and bile duct branches. Some of these challenges may be resolved by using image guidance systems (IGS) to overlay a 3D model of the liver structure onto the liver seen at laparoscopy. Current IGS technologies rely on manual landmark definition or ultrasound for co-registration (alignment of 3D model and in-vivo liver) and do not take organ movement and deformation into account. Image guidance for LLR using a cone beam CT has also been attempted. Our group has developed an IGS that automatically registers a liver model derived from pre-operative CT to the in-vivo liver surface using computer vision techniques. Laparoscope position in relation to the liver is determined by optical tracking. Results from a porcine study and its first application in a patient are presented here. Method Laparoscopic microwave ablation was used to create identifiable liver “lesions” and a CT was obtained in land race pigs under general anaesthesia (GA). One week later, laparoscopic left hepatectomy was performed under GA, using our system. A 46 year old female who presented with an indeterminate liver lesion in the junction of segment 5/6 underwent a hepatic wedge resection under image guidance. Data on system and surgical performance was collected in both studies. Results Experiments were conducted in 5 animals with a successful image overlay achieved in 3 cases. Failure of overlay was attributed to distorted liver anatomy secondary to adhesions formed around regions of ablated liver. Initial registration of the overlayed 3d model was accomplished in less than 10 min (min). The setup and calibration of equipment for the clinical case took 20 min. Initial registration required 3 min and similar to the animal study did not require repetition. Image overlay was successfully achieved and the operation carried out using an ultrasonic scalpel with a total procedure time of 190 min. Estimated blood loss was Conclusion The IGS presented here has been evaluated in both a large animal model and subsequently in a clinical scenario. The system appears to be feasible for clinical use and has benefits in regards to uninterrupted surgical workflow, lack of radiation exposure and automatic compensation for organ motion. Disclosure of interest C. Schneider Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health., S. Thompson Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health., J. Totz Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health., Y. Song Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health., S. Johnsen Grant/ Research Support from: Intelligent Imaging Programme Grant, ref: EP/H046410/1, D. Stoyanov Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health., S. Ourselin Grant/ Research Support from: Intelligent Imaging Programme Grant, ref: EP/H046410/1, K. Gurusamy Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health., D. Hawkes Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health., M. Clarkson Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health., B. Davidson Grant/ Research Support from: This publication presents independent research commissioned by the Health Innovation Challenge Fund (HICF-T4–317), a parallel funding partnership between the Wellcome Trust and the Department of Health. The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health.