Daniel Seider

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.

Intervention Planning of Hepatocellular Carcinoma Radio-Frequency Ablations

We present a software solution for planning and simulating radio-frequency ablation (RFA) treatment for patients suffering from hepatocellular carcinoma. Our software provides the graphical front-end for the results of the EU FP7 project IMPPACT. The main planning application was designed to assist with the identification of minimum-risk setups for RFA probes and generation of evaluable 3D representations of the predicted necrosis zones. Patient-specific mesh data describing the involved anatomic structures are used to individually parameterize the simulation procedure for personalized results. Our software supplies tools for extracting the required anatomic meshes from computed tomography (CT) images and for specifying custom intervention protocols. Data collected during simulations allow for detailed illustration of expected effectiveness and progress of heat-induced necrosis over time. Our software was evaluated positively by medical personnel and has been successfully put into practice at two independent European clinical sites.

RFA-Cut: Semi-automatic Segmentation of Radiofrequency Ablation Zones with and without Needles via Optimal s-t-Cuts

In this contribution, we present a semi-automatic segmentation algorithm for radiofrequency ablation (RFA) zones via optimal s-t-cuts. Our interactive graph-based approach builds upon a polyhedron to construct the graph and was specifically designed for computed tomography (CT) acquisitions from patients that had RFA treatments of Hepatocellular Carcinomas (HCC). For evaluation, we used twelve post-interventional CT datasets from the clinical routine and as evaluation metric we utilized the Dice Similarity Coefficient (DSC), which is commonly accepted for judging computer aided medical segmentation tasks. Compared with pure manual slice-by-slice expert segmentations from interventional radiologists, we were able to achieve a DSC of about eighty percent, which is sufficient for our clinical needs. Moreover, our approach was able to handle images containing (DSC=75.9%) and not containing (78.1%) the RFA needles still in place. Additionally, we found no statistically significant difference (p<;0.423) between the segmentation results of the subgroups for a Mann-Whitney test. Finally, to the best of our knowledge, this is the first time a segmentation approach for CT scans including the RFA needles is reported and we show why another state-of-the-art segmentation method fails for these cases. Intraoperative scans including an RFA probe are very critical in the clinical practice and need a very careful segmentation and inspection to avoid under-treatment, which may result in tumor recurrence (up to 40%). If the decision can be made during the intervention, an additional ablation can be performed without removing the entire needle. This decreases the patient stress and associated risks and costs of a separate intervention at a later date. Ultimately, the segmented ablation zone containing the RFA needle can be used for a precise ablation simulation as the real needle position is known.

Volume visualization in the clinical practice

Volumetric data is common in medicine, geology and engineering, but the O(n3) complexity in data and algorithms has prevented the widespread use of volume graphics. Recently, 3D image processing and visualization algorithms have been parallelized and ported to graphics processing units. Today, medical diagnostics highly depends on volumetric imaging methods that must be visualized in real-time. However, daily clinical practice shows that physicians still prefer simple 2D multi-planar reconstructions over 3D visualizations for intervention planning. Therefore, a very basic question in this context is, if real-time 3D image synthesis is necessary at all. This paper makes four main observations in a clinical context, which are evaluated with 24 independent physicians from three different European hospitals.

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

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.

A prospective development study of software-guided radio-frequency ablation of primary and secondary liver tumors: Clinical intervention modelling, planning and proof for ablation cancer treatment (ClinicIMPPACT)

Introduction Radio-frequency ablation (RFA) is a promising minimal-invasive treatment option for early liver cancer, however monitoring or predicting the size of the resulting tissue necrosis during the RFA-procedure is a challenging task, potentially resulting in a significant rate of under- or over treatments. Currently there is no reliable lesion size prediction method commercially available. Objectives ClinicIMPPACT is designed as multicenter-, prospective-, non-randomized clinical trial to evaluate the accuracy and efficiency of innovative planning and simulation software. 60 patients with early liver cancer will be included at four European clinical institutions and treated with the same RFA system. The preinterventional imaging datasets will be used for computational planning of the RFA treatment. All ablations will be simulated simultaneously to the actual RFA procedure, using the software environment developed in this project. The primary outcome measure is the comparison of the simulated ablation zones with the true lesions shown in follow-up imaging after one month, to assess accuracy of the lesion prediction. Discussion This unique multicenter clinical trial aims at the clinical integration of a dedicated software solution to accurately predict lesion size and shape after radiofrequency ablation of liver tumors. Accelerated and optimized workflow integration, and real-time intraoperative image processing, as well as inclusion of patient specific information, e.g. organ perfusion and registration of the real RFA needle position might make the introduced software a powerful tool for interventional radiologists to optimize patient outcomes.

Semi-automatische Segmentierung von Schädigungszonen in post-interventionellen CT-Daten

Die perkutane Radiofrequenzablation (RFA) ist ein minimalinvasives Verfahren zur thermischen Koagulation von Tumorgewebe und stellt somit eine Alternative zur chirurgischen Entfernung dar. Die Erhitzung wird durch ein elektromagnetisches Wechselfeld erreicht, welches uber eine spezielle Nadelanordnung im Gewebe erzeugt wird. Nach der Intervention wird mit Hilfe von CT-Aufnahmen uberpruft, inwieweit die Ablation vollstandig war, um so das Risiko eines Rezidivs zu minimieren. In diesem Beitrag wurden zwolf RF-Ablationszonen aus post-interventionellen CT-Aufnahmen semiautomatisch segmentiert, um die sehr zeitaufwandige manuelle Inspektion zu unterstutzen. Dazu wurde ein interaktiver, graphbasierter Ansatz verwendet, der kugelformige Objekte bevorzugt. Zur quantitativen und qualitativen Bewertung des Algorithmus wurden manuell segmentierte Schichten von klinischen Experten als Goldstandard verwendet. Zur statistischen Validierung wurde der Dice-Koeffizient herangezogen. Es konnte gezeigt werden, dass der vorgeschlagene Ansatz die Lasionen schneller mit ausreichender Genauigkeit segmentiert und somit fur einen Einsatz in der klinischen Routine geeignet zu sein scheint.