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High Performance Soft-tissue Navigation

Periodic Reporting for period 1 - HiPerNav (High Performance Soft-tissue Navigation)

Reporting period: 2016-11-01 to 2018-10-31

Primary liver cancer, which consists predominantly of hepatocellular carcinoma (HCC), is the fifth most common cancer worldwide and the third most common cause of cancer mortality. The liver is also a frequent target of metastases from other cancer origins, like colorectal, with an estimated 100,000 liver metastases in Europe. Liver resection is the treatment of choice in selected patients with hepatic colorectal metastases, even in recurrent cases, with 5-year survival rates of up to 58%. A successful surgical resection of HCC requires complete removal of the tumour including a safety margin while sparing as much healthy tissue as possible. However, due to technical and clinical difficulties currently only a low percentage of patients are eligible for resection, and the recurrence rate is considerable. In conclusion, there is an urgent need to increase the patient eligibility and improve the survival prognosis after liver interventions (resection or ablation).

Objectives: The overall goal of HiPerNav is to successfully train and educate young researchers (ESRs) in the multidisciplinary field of image-guided interventions. The scientific and clinical goal is to further develop solutions for computer assisted and image guided surgical and interventional procedures for the treatment of primary and secondary liver cancer. It is aimed at improving eligibility and survival prognosis of cancer patients.

The project is organized through a consortium comprising 5 European universities, 2 university hospitals, 2 research organizations and 5 industrial companies, whereas one SME. Oslo University Hospital is the coordinator. In total 16 young researchers (ESRs) will be financed through the project.

The HiPerNav project aims to improve important bottlenecks in soft-tissue navigation:
– effective pre-operative model and planning
– accurate and fast intra-operative model update
– accurate and fast model-to-patient registration
– intuitive user-interaction and effective workflow
– high performance computing by use of GPU

The 14 organizations in the consortium are particularly selected being one of the leading institutions within their field of expertise, and they are all selected to fill specific competences needed to meet the HiPerNav overall goal. The consortium partner organizations are: Oslo University Hospital, University Hospital Bern, NTNU, SINTEF, INRIA, University of Bern, University Paris13, University of Delft, University of Cordoba, CAScination, SIEMENS, NVIDIA, Yes!Delft and Innovation Norway.
Among the 15 ESRs that have started their PhD work in the first period, one third were female.

Andrea Teatini (ESR 1, OUS) is working on model-to-patient registration, with the following PhD title: “Accurate and effective model-to-patient registration for navigation in image guided laparoscopic liver resection”.

Pravda Jith Ray Prasad (ESR 2, OUS) is developing segmentation algorithms for pre-operative and intra-operative liver data from multi-modality images, with a PhD title: “Towards automatic segmentation of liver and lesions using Deep Learning techniques”.

Egidijus Pelanis (ESR 3, OUS) is exploring and evaluating ways of presenting patient-specific liver anatomy with a PhD title: “Clinical assessment and validation of a navigation system during laparoscopic liver interventions”.

Nitin Satpute (ESR 4, UCO) is developing parallel seeded region growing (SRG) algorithms for real time 3D liver and vessel segmentation with a PhD title: “High Performance Seeded Region Growing 3D Liver Segmentation”.

Orestis Zachariadis (ESR 5, UCO) is working on developing new registration algorithms with GPU optimization techniques and developing techniques that will allow fast organ reconstruction and update during intervention surgeries. His PhD title is: “Heterogeneous parallel computing for image registration”.

Rabia Naseem (ESR6, NTNU) is working on denoising and contrast enhancement of liver images for pre-operative and intra-operative surgical planning in her PhD thesis, which has the following title “Cross modality guided image enhancement”.

Shanmugapriya Survarachakan (ESR 7, NTNU) is working on segmentation of liver tumour and hepatic vessels from multi modal 3D data on her PhD thesis entitled “Fast and automatic extraction of liver, vessels and tumor from both pre-operative CT/MR and intra- operative ultrasound in laparoscopic surgery”.

Zohaib Amjad Khan (ESR 8, UP13) is working on the quality assessment part of the image guided surgery workflow for liver cancer resection and ablation surgery. His thesis is titled “Multimodal medical image quality assessment”.

Raluca-Maria Sandu (ESR 9, UNIBE) is working on improving the planning and evaluation of image-guided ablation treatments for liver tumors. The PhD thesis is titled: “Quantitative assessment of ablation treatments for liver tumors – image based efficacy analysis and predictive modelling”.

Benjamin Peter Eigl (ESR 10, CASC) is establishing a computer assisted workflow for minimally-invasive surgery with a focus on pancreatic cancer treatment, with the following PhD title: “Stereotactic, minimally-invasive ablation treatment of pancreatic cancer”.

Ankit Gupta (ESR 11, UNIBE) is working on landmark tracking and registration with the following PhD Title: “Video based instrument tracking and registration in laparoscopic abdominal surgery”.

Jean-Nicolas Brunet (ESR 12, INRIA) is working on the characterization of boundary conditions for biomechanical modelling of the liver with his Ph.D. thesis titled: “new numerical methods for real-time simulation of soft-tissue deformations in surgery assistance”.

Sergei Nikolaev (ESR 13, INRIA) is working on estimation of boundary conditions for liver to improve the predictive capacity of the model. The title of his PhD thesis is: “Identification and characterisation of boundary conditions for patient-specific biomechanical simulation”.

Javier Pérez de Frutos (ESR 14, SINTEF) core research topic is image registration and data fusion for navigation in minimally invasive surgery, being the title of the thesis: “Registration for Improved Preoperative Planning and Intraoperative Navigation in Laparoscopic Surgery”.

Maryam Gholinejad (ESR 15, TUD) is working on optimizing the workflow and user interaction of minimally invasive liver treatments using new planning and navigation platforms. The title of her PhD thesis is: “User interaction and workflow analysis in minimally invasive liver surgery”.
Current solutions for surgical navigation have not been adapted to soft-tissue navigation due to challenges related to organ motion and deformation during the procedure. The HiPerNav project aims to improve several of the bottlenecks existing in current solutions available on the market. This will be done through integrated cross-disciplinary research and development in the following areas:

• Creating effective pre-operative model(s) and planning
• Faster and more accurate intra-operative model updates in combination with prediction of deformation using bio-mechanical models
• Faster and more accurate model-to-patient registration from intra-operative 3D stereoscopic video and ultrasound
• More intuitive user-interaction and effective workflow through detailed procedure analysis
• In general, the use of HPC (e.g. GPU) for smoother, more seamless and better user experience will be explored in all steps of the navigation workflow