Fast and Robust Semi-automatic Liver Segmentation with Haptic Interaction

Vidholm, Erik; Nilsson, Sven Erik G.; Nyström, Ingela

doi:10.1007/11866763_95

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…The CT image was used by Vidholm et al [38] to segment livers semi-automatically by seeded FMM. The CT images are abdominal contrast enhanced venous phase CT images of a patient with either carcinoid or endocrine pancreas tumour.…”

Section: Datasetsmentioning

confidence: 99%

Algorithms for Grey-Weighted Distance Computations

Gedda

2019

Preprint

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With the increasing size of datasets and demand for real time response for interactive applications, improving runtime for algorithms with excessive computational requirements has become increasingly important. Many different algorithms combining efficient priority queues with various helper structures have been proposed for computing grey-weighted distance transforms. Here we compare the performance of popular competitive algorithms in different scenarios to form practical guidelines easy to adopt. The label-setting category of algorithms is shown to be the best choice for all scenarios. The hierarchical heap with a pointer array to keep track of nodes on the heap is shown to be the best choice as priority queue. However, if memory is a critical issue, then the best choice is the Dial priority queue for integer valued costs and the Untidy priority queue for real valued costs.

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Section: Datasetsmentioning

confidence: 99%

Algorithms for Grey-Weighted Distance Computations

Gedda

2019

Preprint

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“…It enables the operator to remain centered inside objects by providing haptic feedback on the boundaries. Application of the haptic interaction in semiautomatic segmentation of the liver from CT scans by using the fast marching algorithm was proposed in [26] with high reproducibility. In order to facilitate efficient 3D interaction during the segmentation of objects with different complexity, by a varying number of live-wire curves, an algorithm using combined stereo graphics and haptics was used in [27].…”

Section: Haptic Segmentationmentioning

confidence: 99%

Fast and accurate semiautomatic haptic segmentation of brain tumor in 3D MRI images

Latifinavid¹,

Bilen²,

Konukseven³

et al. 2016

Turk J Elec Eng & Comp Sci

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Abstract:In this study, a novel virtual reality-based interactive method combined with the application of a graphical processing unit (GPU) is proposed for the semiautomatic segmentation of 3D magnetic resonance imaging (MRI) of the brain. The key point of our approach is to use haptic force feedback guidance for the selection of seed points in a bounded volume with similar intensity and gradient. For the automatic determination of a bounded volume of segmentation in real time, parallel computation on the GPU is used. Automatic segmentation is applied in this adjustable bounded spherical volume with a variable diameter, which is controlled according to the edge map acquired from the gradient map. The haptic force feedback is used in order to guide the user to remain in a volume, where the intensity and gradient change are under a defined threshold range. After each seed point selection, the segmentation algorithm works inside the bounded volume of the ball with an adjusted diameter. The proposed segmentation method based on force and visual feedback with the advantage of adjustable bounded volume is not only accurate and effective in narrow spaces near the boundaries of different layers, but also fast in large homogeneous spaces since the radius of the ball increases in such regions. Parallel programming on the GPU is used for computing gradient change in selected directions, which is needed for the self-adjustment of the sphere diameter. Gradient values are used for calculating the haptic force on the CPU in real time. In this study, two haptic devices are used, one for getting haptic force feedback and the other for camera guidance during 3D visualization. A comparison between manual segmentation of MRI by an expert surgeon and the proposed segmentation algorithm is done. The proposed segmentation procedure is completed 4 times faster than the manual segmentation with similar accuracy.

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“…As 3D interfaces evolved to haptics, so did the 3D user interaction in segmentation applications. Vidholm et al [14,15] used haptics such that seeds for the segmentation could be placed in good spots. Force feedback was applied based on data from MR images so that the user could recognise good seed locations.…”

Section: Force Feedback During Segmentationmentioning

confidence: 99%

Force Feedback to Assist Active Contour Modelling for Tracheal Stenosis Segmentation

Vanacken

Pinho

Sijbers

et al. 2012

Advances in Human-Computer Interaction

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Manual segmentation of structures for diagnosis and treatment of various diseases is a very time-consuming procedure. Therefore, some level of automation during the segmentation is desired, as it often significantly reduces the segmentation time. A typical solution is to allow manual interaction to steer the segmentation process, which is known as semiautomatic segmentation. In 2D, such interaction is usually achieved with click-and-drag operations, but in 3D a more sophisticated interface is called for. In this paper, we propose a semi-automatic Active Contour Modelling for the delineation of medical structures in 3D, tomographic images. Interaction is implemented with the employment of a 3D haptic device, which is used to steer the contour deformation towards the correct boundaries. In this way, valuable haptic feedback is provided about the 3D surface and its deformation. Experiments on simulated and real tracheal CT data showed that the proposed technique is an intuitive and effective segmentation mechanism.

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Fast and Robust Semi-automatic Liver Segmentation with Haptic Interaction

Cited by 10 publications

References 11 publications

Algorithms for Grey-Weighted Distance Computations

Algorithms for Grey-Weighted Distance Computations

Fast and accurate semiautomatic haptic segmentation of brain tumor in 3D MRI images

Force Feedback to Assist Active Contour Modelling for Tracheal Stenosis Segmentation

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