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Introduction: Intraoperative navigation during liver resection remains difficult and requires high radiologic skills because liver anatomy is complex and patient-specific. Augmented reality (AR) during open liver surgery could be helpful to guide hepatectomies and optimize resection margins but faces many challenges when large parenchymal deformations take place. We aimed to experiment a new vision-based AR to assess its clinical feasibility and anatomical accuracy. Patients and Methods: Based on preoperative CT scan 3-D segmentations, we applied a non-rigid registration method, integrating a physics-based elastic model of the liver, computed in real time using an efficient finite element method. To fit the actual deformations, the model was driven by data provided by a single RGB-D camera. Five livers were considered in this experiment. In vivo AR was performed during hepatectomy (n = 4), with manual handling of the livers resulting in large realistic deformations. Ex vivo experiment (n = 1) consisted in repeated CT scans of explanted whole organ carrying internal metallic landmarks, in fixed deformations, and allowed us to analyze our estimated deformations and quantify spatial errors. Results: In vivo AR tests were successfully achieved in all patients with a fast and agile setup installation (< 10 min) and real-time overlay of the virtual anatomy onto the surgical field displayed on an external screen. In addition, an ex vivo quantification demonstrated a 7.9 mm root mean square error for the registration of internal landmarks. Conclusion: These first experiments of a markerless AR provided promising results, requiring very little equipment and setup time, yet providing real-time AR with satisfactory 3D accuracy. These results must be confirmed in a larger prospective study to definitively assess the impact of such minimally invasive technology on pathological margins and oncological outcomes. [ABSTRACT FROM AUTHOR]

Intra-arterial therapy is an effective way of performing chemotherapy or radiation therapy in patients with primary liver cancer (i.e. hepatocellular carcinoma). Although this minimally invasive approach is now an established treatment option, support tools for pre-operative planning and intra-operative assistance might be helpful. We developed an approach for semi-automatic segmentation of computed tomography angiography images of the main arterial branches (required for access path to the treatment site), automatic segmentation of the liver, arterial and venous tree, and interactive segmentation of the tumors (required for procedure-specific planning). This approach was then integrated into a liver-specific workflow within EndoSize® solution, a planning software for endovascular procedures. The main branches extraction approach was qualitatively evaluated inside the software, while the automatic segmentation methods were quantitatively assessed. Main branches extraction provides a success rate of 85% (i.e. all arteries correctly extracted) in a dataset of 172 patients. On public databases, a mean DICE of 0.91, 0.47 and 0.92 was obtained for liver, venous and arterial trees segmentation, respectively. This pipeline is suitable for directly accessing the treatment site, giving anatomic measurements, and visualizing the hepatic trees, liver, and surrounding arteries during the pre-operative planning. HCC: hepatocellular carcinoma; TACE: transarterial chemoembolization; SIRT: selective internal radiation therapy; CT: computed tomography; CTA: computed tomography angiography; AMS: superior mesenteric artery; LGA: left gastric artery; RHA: right hepatic artery; LHA: left hepatic artery; rbHA: right branch of the hepatic artery; lbHA: left branch of the hepatic artery; GDA: gastroduodenal artery; VOI: volume of interest; SD: standard deviation; MICCAI: medical image computing and computer assisted interventions; MR: magnetic resonance [ABSTRACT FROM AUTHOR]