Vessel Segmentation for Ablation Treatment Planning and Simulation

Alhonnoro, Tuomas; Pollari, Mika; Lilja, Mikko; Flanagan, Ronan; Kainz, Bernhard; Muehl, Judith; Mayrhauser, Ursula; Portugaller, Horst; Stiegler, Philipp; Tscheliessnigg, Karlheinz

doi:10.1007/978-3-642-15705-9_6

Cited by 14 publications

(19 citation statements)

References 19 publications

(23 reference statements)

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“…The data has been used for various applications. In [6] we could show that our datasets allow for vessel segmentation with an error less than one voxel. [7,8] discuss how to use these datasets for access path decision support for minimal invasive interventions and [9] use the segmentation results from these datasets to build a model for RFA ablations, which is able to accurately predict the cooling effect of vessels during the RFA procedure.…”

Section: Applicationsmentioning

confidence: 87%

High-resolution contrast enhanced multi-phase hepatic Computed Tomography data fromaporcine Radio-Frequency Ablation study

Kainz

Voglreiter

Sereinigg

et al. 2014

2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)

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Data below 1 mm voxel size is getting more and more common in the clinical practice but it is still hard to obtain a consistent collection of such datasets for medical image processing research. With this paper we provide a large collection of Contrast Enhanced (CE) Computed Tomography (CT) data from porcine animal experiments and describe their acquisition procedure and peculiarities. We have acquired three CE-CT phases at the highest available scanner resolution of 57 porcine livers during induced respiratory arrest. These phases capture contrast enhanced hepatic arteries, portal venous veins and hepatic veins. Therefore, we provide scan data that allows for a highly accurate reconstruction of hepatic vessel trees. Several datasets have been acquired during Radio-Frequency Ablation (RFA) experiments. Hence, many datasets show also artificially induced hepatic lesions, which can be used for the evaluation of structure detection methods.

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Section: Applicationsmentioning

confidence: 87%

High-resolution contrast enhanced multi-phase hepatic Computed Tomography data fromaporcine Radio-Frequency Ablation study

Kainz

Voglreiter

Sereinigg

et al. 2014

2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)

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“…With a gap of 3 mm between neighboring slices the changes in vessel forms are not smooth enough. Most cases where vessel enhancement was used so far had much smaller slice thickness, with sometimes even isotropic voxels 20,30,32,33,36. Also, hepatic vessels as seen in medical images, especially major vessels, do not have a shape of a perfect tube.…”

Section: Discussionmentioning

confidence: 99%

“…Similar treatment planning process is well-established in radiotherapy where it has been in use for decades 18. Generation of models from medical images for subsequent numerical calculations has also been used as a part of treatment planning for radiofrequency ablation (RFA) of liver tumors 19,20…”

Section: Introductionmentioning

confidence: 99%

“…First attempts were based solely on thresholding24 and region growing 25,26. The evolution of highly popular methods for enhancement of tubular structures27–29 resulted in their combinations with thresholding30 and region growing 20,31–33. Other than tube-enhancing filtering the traditional methods of segmentation were enhanced through use of Gaussian mixture models34,35 and by utilizing morphology of the vascular tree through centerline extraction 20,31.…”

Section: Introductionmentioning

confidence: 99%

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Segmentation of hepatic vessels from MRI images for planning of electroporation-based treatments in the liver

Marčan

Pavliha

Mušič

et al. 2014

Radiology and Oncology

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Introduction.Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses delivered to tissue via electrodes. To ensure that the whole tumor is covered by the sufficiently high electric field, accurate numerical models are built based on individual patient geometry. For the purpose of reconstruction of hepatic vessels from MRI images we searched for an optimal segmentation method that would meet the following initial criteria: identify major hepatic vessels, be robust and work with minimal user input.Materials and methods.We tested the approaches based on vessel enhancement filtering, thresholding, and their combination in local thresholding. The methods were evaluated on a phantom and clinical data.ResultsResults show that thresholding based on variance minimization provides less error than the one based on entropy maximization. Best results were achieved by performing local thresholding of the original de-biased image in the regions of interest which were determined through previous vessel-enhancement filtering. In evaluation on clinical cases the proposed method scored in average sensitivity of 93.68%, average symmetric surface distance of 0.89 mm and Hausdorff distance of 4.04 mm.ConclusionsThe proposed method to segment hepatic vessels from MRI images based on local thresholding meets all the initial criteria set at the beginning of the study and necessary to be used in treatment planning of electroporation-based treatments: it identifies the major vessels, provides results with consistent accuracy and works completely automatically. Whether the achieved accuracy is acceptable or not for treatment planning models remains to be verified through numerical modeling of effects of the segmentation error on the distribution of the electric field.

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“…Conversion to the skeletal domain reveals the vessel tree structure for qualitative analysis, and the transform is approximately invertible, thus it can be used as an intermediate step within the segmentation pipeline. In our previous work [43], this method was proven to be high performing and capable of extracting 97 and 75 per cent of the vessels equal to or above the radius of 2.0 or 1.0 voxel units, respectively. With regard to sensitivity, the results showed improvements over the existing methods.…”

Section: (C) Vessel Segmentationmentioning

confidence: 99%

Image-based multi-scale modelling and validation of radio-frequency ablation in liver tumours

Payne

Flanagan²,

Pollari

et al. 2011

Phil. Trans. R. Soc. A.

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The treatment of cancerous tumours in the liver remains clinically challenging, despite the wide range of treatment possibilities, including radio-frequency ablation (RFA), highintensity focused ultrasound and resection, which are currently available. Each has its own advantages and disadvantages. For non-or minimally invasive modalities, such as RFA, considered here, it is difficult to monitor the treatment in vivo. This is particularly problematic in the liver, where large blood vessels act as heat sinks, dissipating delivered heat and shrinking the size of the lesion (the volume damaged by the heat treatment) locally; considerable experience is needed on the part of the clinician to optimize the heat treatment to prevent recurrence. In this paper, we outline our work towards developing a simulation tool kit that could be used both to optimize treatment protocols in advance and to train the less-experienced clinicians for RFA treatment of liver tumours. This tool is based on a comprehensive mathematical model of bio-heat transfer and cell death. We show how simulations of ablations in two pigs, based on individualized imaging data, compare directly with experimentally measured lesion sizes and discuss the likely sources of error and routes towards clinical implementation. This is the first time that such a 'loop' of mathematical modelling and experimental validation in vivo has been performed in this context, and such validation enables us to make quantitative estimates of error.

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Vessel Segmentation for Ablation Treatment Planning and Simulation

Cited by 14 publications

References 19 publications

High-resolution contrast enhanced multi-phase hepatic Computed Tomography data fromaporcine Radio-Frequency Ablation study

High-resolution contrast enhanced multi-phase hepatic Computed Tomography data fromaporcine Radio-Frequency Ablation study

Segmentation of hepatic vessels from MRI images for planning of electroporation-based treatments in the liver

Image-based multi-scale modelling and validation of radio-frequency ablation in liver tumours

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