Verisuonten segmentointi ablaatiohoidon suunnittelua ja simulointia varten

In this work, a novel semiautomatic hybrid method for the segmentation of hepatic vasculature is presented, intended for numerical simulation of radio frequency ablation (RFA). The method combines several of fundamental methods - multiscale enhancement filters, ridge-based region growning and skeleton-based post processing - in a new elegant way. The proposed pyramid computation can provide full segmentation results in few minutes. In addition, interactive tools were developed for exploration and manual editing of the segmented vessel tree designed especially from the simulation point of view. The method was evaluated, both qualitatively and quantitatively, using four instances of three-phase contrast enhanced computed tomography CT) images of porcine liver and additional set of clinical routine human CT images. While qualitative visualization is often the only evaluation in regard of vessel segmentation, this work has taken important step to provide a new evaluation protocol and full quantitative validation of the method. The results prove that the proposed technique improves the accuracy of the vessel segmentation in comparison to previous approaches. In addition, the method's suitability for simulation purposes has been illustrated. Specifically, this method is capable of extracting 97% of hepatic vessels equal or above the critical threshold (3.0 mm in diameter) for ablation heat propagation. But accuracy does not fall until subvoxel resolution.Työssä kehitettiin puoliautomaattinen menetelmä maksan verisuonten segmentointiin; erityisesti radiotaajuusablaatio (RFA)-hoidon suunnittelua ja simulointia varten. Nyt esitettävä menetelmä yhdistelee olennaisia elementtejä - moniresoluutio kuvanparannusta, kuvanharjanteisiin perustuvaa alueenkasvutusta, luurankomalliperusteista jälkikäsittelyä - tavalla, joka on uusi. Esitetty pyramidiapproksimaatio suoriutuu laskennasta muutamissa minuuteissa. Lisäksi kehitettiin interaktiivisia työkaluja monimuotoisen suonistopuun visualisointia ja editointia varten. Menetelmä testattiin sekä kvalitatiivisesti että kvantitatiivisesti käyttäen neljää varjoainetietokonetomografia kuvasarjaa sian maksasta. Lisäksi käytettiin muutamia kliinisiä kuvia. Kvantitatiivista evaluaatiota varten kehitettiin uusi protokolla, joka poikkeaa vallitsevasta, visuaaliseen evaluointiin perustuvasta käytännöstä. Työssä saavutetut tulokset osoittavat, että kehitetyllä menetelmällä voidaan merkittävästi parantaa verisuonten segmentointia. Erityisesti, menetelmä pystyy havaitsemaan 97 % halkaisijaltaan 3.0 mm tai suuremmista maksan verisuonista, joiden tunnetaan vaikuttavan lämmön jakautumiseen RFA-hoidossa. Menetelmän tarkkuus säilyy kuitenkin tyydyttävänä aina vokseliresoluutioon asti

Interactive registration of 2D histology and 3D CT data for assessment of radiofrequency ablation treatment

Histological investigation of a lesion induced by radiofrequency ablation (RFA) treatment provides ground-truth about the true lesion size, thus verifying the success or failure of the RFA treatment. This work presents a framework for registration of two-dimensional large-scale histological sections and three-dimensional CT data typically used to guide the RFA intervention. The focus is on the developed interactive methods for reconstruction of the histological volume data by fusion of histological and high-resolution CT (MicroCT) data and registration into CT data based on natural feature points. The framework is evaluated using RFA interventions in a porcine liver and applying medically relevant metrics. The results of registration are within clinically required precision targets; thus the developed methods are suitable for validation of the RFA treatment

15. Intelligent data engineering

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Interactive Volumetry Of Liver Ablation Zones

Percutaneous radiofrequency ablation (RFA) is a minimally invasive technique that destroys cancer cells by heat. The heat results from focusing energy in the radiofrequency spectrum through a needle. Amongst others, this can enable the treatment of patients who are not eligible for an open surgery. However, the possibility of recurrent liver cancer due to incomplete ablation of the tumor makes post-interventional monitoring via regular follow-up scans mandatory. These scans have to be carefully inspected for any conspicuousness. Within this study, the RF ablation zones from twelve post-interventional CT acquisitions have been segmented semi-automatically to support the visual inspection. An interactive, graph-based contouring approach, which prefers spherically shaped regions, has been applied. For the quantitative and qualitative analysis of the algorithm’s results, manual slice-by-slice segmentations produced by clinical experts have been used as the gold standard (which have also been compared among each other). As evaluation metric for the statistical validation, the Dice Similarity Coefficient (DSC) has been calculated. The results show that the proposed tool provides lesion segmentation with sufficient accuracy much faster than manual segmentation. The visual feedback and interactivity make the proposed tool well suitable for the clinical workflow.Peer reviewe