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      Up-to-date intraoperative computer assisted solutions for liver surgery

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          Abstract

          Computer assisted surgical planning allowed for a better selection of patients, evaluation of operative strategy, appropriate volumetric measurements, identification of anatomical risks, definition of tumour resection margins and choice of surgical approach in liver oncologic resections and living donor liver transplantations. Although preoperative computer surgical analysis has been widely used in daily clinical practice, intraoperative computer assisted solutions for risk analysis and navigation in liver surgery are not widely available or still under clinical evaluation. Computer science technology can efficiently assist modern surgeons during complex liver operations, mainly by providing image guidance with individualized 2D images and 3D models of the various anatomical and pathological structures of interest. Intraoperative computer assisted liver surgery is particularly useful in complex parenchyma-sparing hepatectomies, for intraoperative risk analysis and for the effective treatment of colorectal metastases after neoadjuvant therapy or when they are multiple. In laparoscopic liver surgery, intraoperative computer aid is definitively more important as, apart from a restricted field of view, there is also loss of the fine haptic feedback. Intraoperative computer assisted developments face challenges that prevent their application in daily clinical practice. There is a vast variety of studies regarding intraoperative computer assisted liver surgery but there are no clear objective measurements in order to compare them and select the most effective solutions. An overview of up-to-date intraoperative computer assisted solutions for liver surgery will be discussed.

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          Most cited references48

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          Three-dimensional volume rendering of spiral CT data: theory and method.

          Three-dimensional (3D) medical images of computed tomographic (CT) data sets can be generated with a variety of computer algorithms. The three most commonly used techniques are shaded surface display, maximum intensity projection, and, more recently, 3D volume rendering. Implementation of 3D volume rendering involves volume data management, which relates to operations including acquisition, resampling, and editing of the data set; rendering parameters including window width and level, opacity, brightness, and percentage classification; and image display, which comprises techniques such as "fly-through" and "fly-around," multiple-view display, obscured structure and shading depth cues, and kinetic and stereo depth cues. An understanding of both the theory and method of 3D volume rendering is essential for accurate evaluation of the resulting images. Three-dimensional volume rendering is useful in a wide variety of applications but is just now being incorporated into commercially available software packages for medical imaging. Although further research is needed to determine the efficacy of 3D volume rendering in clinical applications, with wider availability and improved cost-to-performance ratios in computing, 3D volume rendering is likely to enjoy widespread acceptance in the medical community.
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            Liver segmentation from computed tomography scans: a survey and a new algorithm.

            In the recent years liver segmentation from computed tomography scans has gained a lot of importance in the field of medical image processing since it is the first and fundamental step of any automated technique for the automatic liver disease diagnosis, liver volume measurement, and 3D liver volume rendering. In this paper we report a review study about the semi-automatic and automatic liver segmentation techniques, and we describe our fully automatized method. The survey reveals that automatic liver segmentation is still an open problem since various weaknesses and drawbacks of the proposed works must still be addressed. Our gray-level based liver segmentation method has been developed to tackle all these problems; when tested on 40 patients it achieves satisfactory results, comparable to the mean intra- and inter-observer variation. We believe that our technique outperforms those presented in the literature; nevertheless, a common test set with its gold standard traced by experts, and a generally accepted performance measure are required to demonstrate it.
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              Augmented reality in neurosurgery

              Neurosurgery is a medical specialty that relies heavily on imaging. The use of computed tomography and magnetic resonance images during preoperative planning and intraoperative surgical navigation is vital to the success of the surgery and positive patient outcome. Augmented reality application in neurosurgery has the potential to revolutionize and change the way neurosurgeons plan and perform surgical procedures in the future. Augmented reality technology is currently commercially available for neurosurgery for simulation and training. However, the use of augmented reality in the clinical setting is still in its infancy. Researchers are now testing augmented reality system prototypes to determine and address the barriers and limitations of the technology before it can be widely accepted and used in the clinical setting.
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                Author and article information

                Contributors
                Journal
                World J Gastrointest Surg
                WJGS
                World Journal of Gastrointestinal Surgery
                Baishideng Publishing Group Inc
                1948-9366
                27 January 2019
                27 January 2019
                : 11
                : 1
                : 1-10
                Affiliations
                Surgical Oncology, OLYMPION General Clinic of Patras, Patras 26442, Greece. azygomalas@ 123456upatras.gr
                Department of Surgery, University Hospital of Patras, Patras 26500, Greece
                Author notes

                Author contributions: All authors equally contributed to this paper with conception and design of the study, literature review and analysis, drafting and critical revision and editing, and final approval of the final version.

                Corresponding author: Apollon Zygomalas, MD, MSc, PhD, FACS, Surgeon, Surgical Oncology, OLYMPION General Clinic of Patras, Volou and Meilichou, Patras 26443, Greece. azygomalas@ 123456upatras.gr

                Telephone: +30-2610-464100 Fax: +30-2610-464115

                Article
                jWJGS.v11.i1.pg1
                10.4240/wjgs.v11.i1.1
                6354070
                30705734
                d92a5714-93b2-46dd-9ec4-1938f91f70b3
                ©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.

                This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.

                History
                : 29 August 2018
                : 12 December 2018
                : 29 December 2018
                Categories
                Minireviews

                computer assisted surgery,liver,intraoperative,3d models,navigation,image guidance,augmented reality,hepatic surgery,hepatectomy

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