<abstract xmlns="http://www.w3.org/1999/xhtml"> 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. Results. Results 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. Conclusions. The 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.</abstract>

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. Results. Results 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. Conclusions. The 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.

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

University of Ljubljana, Faculty of Electrical Engineering, Ljubljana, Slovenia

Institute of Oncology Ljubljana, Ljubljana, Slovenia

Clinical Department for Diagnostic and Interventional Radiology, Clinical Hospital “Dubrava”, Zagreb, Croatia

University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia

Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia

Radiology and Oncology

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Introduction. Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses delivered to tissue via...

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

Publié en ligne: 10 juil. 2014

Pages: 267 - 281

Reçu: 09 janv. 2014

Accepté: 10 avr. 2014

DOI: https://doi.org/10.2478/raon-2014-0022

Mots clés
electrochemotherapy, non-thermal irreversible electroporation, treatment planning, hepatic vessel segmentation, non-invasive tumor treatments, MRI of liver

© by Damijan Miklavcic

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

Publié en ligne: 10 juil. 2014

Pages: 267 - 281

Reçu: 09 janv. 2014

Accepté: 10 avr. 2014

DOI: https://doi.org/10.2478/raon-2014-0022

Mots cléselectrochemotherapy, non-thermal irreversible electroporation, treatment planning, hepatic vessel segmentation, non-invasive tumor treatments, MRI of liver

© by Damijan Miklavcic

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Mots clés
electrochemotherapy, non-thermal irreversible electroporation, treatment planning, hepatic vessel segmentation, non-invasive tumor treatments, MRI of liver