Liver Surgery Planning Using Virtual Reality

Reitinger, Bernhard; Bornik, Alexander; Beichel, Reinhard; Schmalstieg, Dieter

doi:10.1109/mcg.2006.131

Cited by 123 publications

(79 citation statements)

References 7 publications

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“…In the case of liver resection the most typical study is the contrast enhanced CT that enables the visualisation of vessels. The definition of the resection path and plane is done in a preoperative stage using a planning tool like those described in [3]. Once this plane is defined, the contents of the Resection Map can be generated: the resection plane, the main veins and the resection targets (tumours).…”

Section: Implementation and Concept Validationmentioning

confidence: 99%

“…Major research work in computer aided surgical systems for liver surgery is focused on providing preoperative planning support [1][2][3][4][5]. These systems perform image segmentation and 3D reconstruction of liver, tumour, and vessel structures.…”

Section: Introductionmentioning

confidence: 99%

“…Their resection proposals consider tumour size and position, the relation of the tumour to the vessel structure and user-defined security margins. User interaction is generally in 2D rendered images, or even involving a 3D virtual environment [3]. These systems are yet not focusing on the presentation of information intraoperatively, and the surgeon is forced to rely on his memory and ability to translate the preoperative figures into the operative site [1,6,7].…”

Section: Introductionmentioning

confidence: 99%

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The Resection Map, a proposal for intraoperative hepatectomy guidance

et al. 2008

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Objective To propose a new concept of an intra-operative 3D visualisation system to support hepatectomies. This system aims at improving the transfer of pre-operative planning into the intra-operative stage, both in laparoscopic and open approaches. Materials and methods User (surgeon) centred developmental process to identify the surgical requirements is applied. The surgical workflow of hepatectomy is analyzed, including observations of liver surgeries and focus group sessions. Based on this analysis, specifications for the "Resection Map" are defined. A first implementation is developed, and preliminary clinical acceptance results are gathered. Results Control of main veins and tumour margins are the two critical aspects. The "Resection Map" provides an intuitive visualisation of structures nearby the resection plane without any registration to the patient space.

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Section: Implementation and Concept Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Resection Map, a proposal for intraoperative hepatectomy guidance

et al. 2008

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“…For example, the planning stage proposed in [10] needs branch labelling which is the most time-consuming step in the planning procedure and usually involves some trial and error on the user's part. Mint Liver, a novel 3D image analysis software for liver resection, has to be used by experienced hepatic surgeons for designing the new transection plan.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A Fast Algorithm for Liver Surgery Planning

Xinbo²,

Klette

et al. 2013

Discrete Geometry for Computer Imagery

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Abstract. Assume that a simplified liver model consists of some vein cells and liver cells. Such a liver model contains two kinds of components, the vein component and the liver components, each of them consists of cells which are 26-connected. The vein component has a tree-shape topology. Suppose that the vein component has already been cut into two parts, and one of them is diseased. Liver surgery planning systems need to design an algorithm to decompose the liver components into two kinds of subsets, one (usually just one component) that has been affected by the diseased vein component while the other one is still healthy. So far, existing algorithms depend heavily on surgeons' personal expertise to detect the diseased liver component which needs to be removed. We propose an efficient algorithm for computing the diseased liver component which is based on the diseased vein component, and not on surgeons' personal manipulations.

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“…The image segmentation is the first and essential process in many medical applications including analysis of anatomical structure [1], lesion detection [2], volume measurement and surgical planning [3]. This process is traditionally performed by radiologists or medical specialists who use their knowledge and experience to manually trace the objects on each image or slice.…”

Section: Introductionmentioning

confidence: 99%

A Model Optimization Approach to the Automatic Segmentation of Medical Images

Afifi

Nakaguchi

Tsumura

et al. 2010

IEICE Trans. Inf. & Syst.

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SUMMARYThe aim of this work is to develop an efficient medical image segmentation technique by fitting a nonlinear shape model with presegmented images. In this technique, the kernel principle component analysis (KPCA) is used to capture the shape variations and to build the nonlinear shape model. The pre-segmentation is carried out by classifying the image pixels according to the high level texture features extracted using the over-complete wavelet packet decomposition. Additionally, the model fitting is completed using the particle swarm optimization technique (PSO) to adapt the model parameters. The proposed technique is fully automated, is talented to deal with complex shape variations, can efficiently optimize the model to fit the new cases, and is robust to noise and occlusion. In this paper, we demonstrate the proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans and the obtained results are very hopeful.

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Liver Surgery Planning Using Virtual Reality

Cited by 123 publications

References 7 publications

The Resection Map, a proposal for intraoperative hepatectomy guidance

The Resection Map, a proposal for intraoperative hepatectomy guidance

A Fast Algorithm for Liver Surgery Planning

A Model Optimization Approach to the Automatic Segmentation of Medical Images

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