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In-depth Multicenter Workflow Analysis of Liver Tumor Ablations for the Development of a Novel Computer-aided Software Tool

  • December 2015
DOI:10.13140/RG.2.1.2792.6803
  • Conference: 101th Annual Meeting of The Radiological Society of North America (RSNA)
Authors:
Jan Egger at University Hospital Essen (AöR)
Jan Egger
  • University Hospital Essen (AöR)
Philip Voglreiter at Graz University of Technology
Philip Voglreiter
Mark Dokter at Know-Center
Mark Dokter
Michael Hofmann at Graz University of Technology
Michael Hofmann
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Abstract and Figures

Radiofrequency ablation (RFA) is a percutaneous procedure for cancer treatment, which belongs to the minimally invasive and image-guided techniques. Cancer cells are heated up and destroyed by focusing energy in the RF spectrum through a needle. Thus, needle placement and the right amount of energy play the crucial roles for a therapeutic success. However, there is no standardized practice for needle guidance, which can depend on the equipment, or personal preferences (like 2D/3D Ultrasound (US) or CT-guidance). The aim was to assess the clinical feasibility of a common multicenter workflow of thermally induced liver lesions for the development of a novel computer-aided RFA software tool.
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In-depth Multicenter Workflow Analysis of Liver Tumor Ablations for
the Development of a Novel Computer-aided Software Tool
J. Egger a,b, P. Voglreiter a, M. Dokter a. M. Hofmann a, H. F. Busse c, D. Seider c, P. Brandmaier c
R. Rautio d, G. Zettel e, B. Schmerböck e, M. van Amerongen f, S. Jenniskens f, M. Kolesnik g
B. Kainz h, R. B. Sequeiros i, H. Portugaller j, P. Stiegler e, J. Futterer f, D. Schmalstieg a
M. Moche c
a Institute for Computer Graphics and Vision, Graz University of Technology, Austria.
b BioTechMed-Graz, Austria.
c Department of Diagnostic and Interventional Radiology, Leipzig University Hospital, Germany.
d Department of Radiology, Turku University Hospital, Turku, Finland.
e Department of Surgery, Division of Transplantation Surgery, Medical University of Graz, Austria.
f Department of Radiology, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands.
g Institute for Applied Information Technology, Fraunhofer Ges., Schloss Birlinghoven, Sankt Augustin, Germany.
h Division of Imaging Sciences and Biomedical Engineering, King's College, London, UK.
i South Western Finland Imaging Centre, Turku University Hospital, Turku, Finland.
j Department of Radiology, Medical University of Graz, Austria.
BACKGROUND
Radiofrequency ablation (RFA) is a percutaneous procedure for cancer treatment, which belongs to the
minimally invasive and image-guided techniques. Cancer cells are heated up and destroyed by focusing
energy in the RF spectrum through a needle. Thus, needle placement and the right amount of energy
play the crucial roles for a therapeutic success. However, there is no standardized practice for needle
guidance, which can depend on the equipment, or personal preferences (like 2D/3D Ultrasound (US) or
CT-guidance). The aim was to assess the clinical feasibility of a common multicenter workflow of
thermally induced liver lesions for the development of a novel computer-aided RFA software tool.
EVALUATION
RFAs of primary liver tumors where observed at four different clinical centers around Europe and
discussed between medical and technical partners. As primary imaging modalities, CT scanners from the
major manufactures (GE, Philips, Siemens and Toshiba) have been used. For needle placement, several
navigation techniques were applied: CT-guidance, 2D US guidance, CT-US fusion; although the final
needle position was always confirmed by a CT scan. Thus, a heat simulation can be used to prepare
patient-specific ablation protocols, especially for cases where other organs like the diaphragm are close
by. In addition, a study among 269 patients showed that the safety margin around the ablated tumor is
the only independent factor that influenced tumor recurrence. Thus, we developed a bivariate
visualization and a levels-of-detail-based distance algorithm supporting radiologists in both: RFA planning
and monitoring. The algorithm has been evaluated by 13 interventional radiologists within multiple tasks
of an official approved Visual Saliency study, showing a significant improvement of 42% (3.8±0.8 vs
5.4±0.4).
DISCUSSION
Needle placement and RF simulation are crucial factors for a complete and successful ablation of liver
tumors, which can be assisted by a software tool that results from studying the interventions of several
experts.
CONCLUSION
Reliable computer-aided support of the complex procedure of liver RFAs by study multicenter workflows
to lay the foundation for a novel software tool.
REFERENCES
[1] H. F. Busse, et al. Novel Semiautomatic Real-time CT Segmentation Tool and Preliminary Clinical
Evaluation on Thermally Induced Lesions in the Liver, 100th Annual Meeting of The Radiological
Society of North America (RSNA), PHS 171, December 2014.
[2] J. Egger, et al. RFA-Cut: Semi-automatic Segmentation of Radiofrequency Ablation Zones with and
without Needles via Optimal s-t-Cuts. 37th Annual International Conference of the IEEE Engineering
in Medicine and Biology Society, Milan, Italy, IEEE Press, Aug. 2015.
[3] J. Egger, et al. Semi-automatic Segmentation of Ablation zones in post-interventional CT Data.
Proceedings of Bildverarbeitung für die Medizin (BVM), Springer Press, pp. 281-286, 2015.
[4] J. Egger, et al. Interactive Volumetry of Liver Ablation Zones. Sci. Rep. 5, 15373, October 2015.
[5] J. Egger. Refinement-Cut: User-Guided Segmentation Algorithm for Translational Science. Sci. Rep.
4, 5164, June 2014.
[6] J. Egger, et al. Nugget-Cut: A Segmentation Scheme for Spherically- and Elliptically-Shaped 3D
Objects. 32nd Annual Symposium of the German Association for Pattern Recognition (DAGM), LNCS
6376, pp. 383-392, Springer Press, Darmstadt, Germany, Sep. 2010.
[7] J. Egger, et al. Template-Cut: A Pattern-Based Segmentation Paradigm. Sci. Rep., 2, 420, May 2012.
[8] J. Egger, et al. Interactive-Cut: Real-Time Feedback Segmentation for Translational Research.
Comput Med Imaging Graph. 38(4):285-95, June 2014.
[9] J. Egger, et al. A medical software system for volumetric analysis of cerebral pathologies in
magnetic resonance imaging (MRI) data. J Med Syst. 2012 Aug; 36(4):2097-109.
[10] J. Egger, et al. A Software System for Stent Planning, Stent Simulation and Follow-Up Examinations
in the Vascular Domain. 22nd IEEE International Symposium on Computer-Based Medical Systems,
Albuquerque, New Mexico, USA, IEEE Press, ACM/SIGAPP, pp. 1-7, Aug. 2009.
ACKNOWLEDGMENTS
This work received funding from two European Union projects in FP7:
Clinical Intervention Modelling, Planning and Proof for Ablation Cancer Treatment (ClinicIMPPACT)
Grant agreement no. 610886
http://www.clinicimppact.eu
Generic Open-end Simulation Environment for Minimally Invasive Cancer Treatment (GoSmart)
Grant agreement no. 600641
http://www.gosmart-project.eu
Dr. Dr. Jan Egger receives funding from BioTechMed-Graz (“Hardware accelerated intelligent medical
imaging”).
CITE THIS ABSTRACT
Egger,J, Voglreiter,P, Dokter,M, Hofmann,M, Busse,H, Seider,D, Brandmaier,P, Rautio,R, Zettel,G, Schmerböck,B, van
Amerongen,M, Jenniskens,S, Kolesnik,M, Kainz,B, Sequeiros,R, Portugaller,H, Stiegler,P, Futterer,J, Schmalstieg,D, Moche,M, In-
depth Multicenter Workflow Analysis of Liver Tumor Ablations for the Development of a Novel Computer-aided Software Tool.
Radiological Society of North America 2015 Scientific Assembly and Annual Meeting, November 29 - December 4, 2015, IN243-
SD-WEA4, Chicago IL.
ResearchGate has not been able to resolve any citations for this publication.
Interactive Volumetry Of Liver Ablation Zones
Article
Full-text available
  • Oct 2015
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.
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Novel Semiautomatic Real-time CT Segmentation Tool and Preliminary Clinical Evaluation on Thermally Induced Lesions in the Liver
Conference Paper
Full-text available
  • Dec 2014
PURPOSE Quantitative, image-based assessments of lesion volume and shape improve the correctness of follow-up reports and potentially influence therapeutical decisions but are usually laborious and time-consuming. The goal was to assess the clinical feasibility of a novel real-time CT segmentation tool on thermally induced liver lesions. METHOD AND MATERIALS CT data were available from patients with unresectable, primary liver tumors that underwent CT-guided radiofrequency ablation at our institution (MX8000/Brilliance, Philips, NL; StarBurst, Angiodynamics, NY). Two radiological readers retrospectively segmented 12 lesions in CT images using a manual contouring tool under MeVisLab (Bremen, GER). One independent reader used a novel real-time segmentation tool derived from a previous batch application for the brain and prostate. The algorithm starts with a spherical template of 3D nodes and edges outside the lesion. Nodes are continuously adapted by sending rays from a user-defined seed point inside the lesion through the surface of the polyhedron. Key parameters like stiffness and number of nodes were defined on a training dataset. The user can visually explore and modify the 3D result on the fly. The Dice Similarity Coefficient (DSC) was used to measure the agreement of two segmentations. Differences in manual processing times tP and measured lesion volumes VL were analyzed by two-sided paired t-tests (α=0.05) using SPSS 20 (IBM, NY). RESULTS Measured VL was 10.0 – 122.6 ml (mean 36.0 ml) and tP was 0:48 – 8:16 min (mean 3:13 min). Differences in VL (mean 0.3 ml, p=0.639) and tP between both readers (mean 0:22 min, p=0.200) were not significant and the mean DSC was 89 % (82 – 93 %). Differences between automatically and manually segmented (mean of both readers) VL were somewhat larger but not significantly (mean -3.0 ml, p=0.305). The corresponding mean DSC was 77 % (68 – 85 %). In ten cases, the seed point or key parameters were slightly refined, which took less than 1 min, and in two cases, no further interaction was required. CONCLUSION Reliable estimates of lesion volumes and shapes could be obtained on-the-fly by using a novel real-time segmentation tool in patients undergoing radiofrequency in the liver. CLINICAL RELEVANCE/APPLICATION Lesion volume and shape, potential factors for therapeutic decisions, can be reliably estimated and monitored with a real-time CT segmentation tool with immediate visual feedback in under a minute.
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Refinement-Cut: User-Guided Segmentation Algorithm for Translational Science
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Full-text available
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In this contribution, a semi-automatic segmentation algorithm for (medical) image analysis is presented. More precise, the approach belongs to the category of interactive contouring algorithms, which provide real-time feedback of the segmentation result. However, even with interactive real-time contouring approaches there are always cases where the user cannot find a satisfying segmentation, e.g. due to homogeneous appearances between the object and the background, or noise inside the object. For these difficult cases the algorithm still needs additional user support. However, this additional user support should be intuitive and rapid integrated into the segmentation process, without breaking the interactive real-time segmentation feedback. I propose a solution where the user can support the algorithm by an easy and fast placement of one or more seed points to guide the algorithm to a satisfying segmentation result also in difficult cases. These additional seed(s) restrict(s) the calculation of the segmentation for the algorithm, but at the same time, still enable to continue with the interactive real-time feedback segmentation. For a practical and genuine application in translational science, the approach has been tested on medical data from the clinical routine in 2D and 3D.
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Template-Cut: A Pattern-Based Segmentation Paradigm
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Full-text available
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We present a scale-invariant, template-based segmentation paradigm that sets up a graph and performs a graph cut to separate an object from the background. Typically graph-based schemes distribute the nodes of the graph uniformly and equidistantly on the image, and use a regularizer to bias the cut towards a particular shape. The strategy of uniform and equidistant nodes does not allow the cut to prefer more complex structures, especially when areas of the object are indistinguishable from the background. We propose a solution by introducing the concept of a "template shape" of the target object in which the nodes are sampled non-uniformly and non-equidistantly on the image. We evaluate it on 2D-images where the object's textures and backgrounds are similar, and large areas of the object have the same gray level appearance as the background. We also evaluate it in 3D on 60 brain tumor datasets for neurosurgical planning purposes.
View
RFA-cut: Semi-automatic segmentation of radiofrequency ablation zones with and without needles via optimal s-t-cuts
Conference Paper
  • Aug 2015
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.
View
View
Interactive-Cut: Real-Time Feedback Segmentation for Translational Research
Article
  • Jun 2014
In this contribution, a scale-invariant image segmentation algorithm is introduced that “wraps” the algorithm's parameters for the user by its interactive behavior, avoiding the definition of “arbitrary” numbers, (s)he cannot really understand. Therefore, we designed a specific graph-based segmentation method that only requires a single seed-point inside the target-structure from the user and is thus particularly suitable for immediate processing and interactive, real-time adjustments by the user. In addition, color or gray value information that is needed for the approach can be automatically extracted around the user-defined seed point. Furthermore, the graph is constructed in such a way, so that a polynomial-time mincut computation can provide the segmentation result within a second on an up-to-date computer. The presented algorithm has been evaluated with fixed seed points mainly on 2D and 3D medical image data, such as brain tumors, cerebral aneurysms and vertebral bodies. Direct comparison of the obtained automatic segmentation results with costlier, manual slice-by-slice segmentations performed by trained physicians, suggest a strong medical relevance of this interactive approach.
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A software system for stent planning, stent simulation and follow-up examinations in the vascular domain
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A Medical Software System for Volumetric Analysis of Cerebral Pathologies in Magnetic Resonance Imaging (MRI) Data
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In this contribution, a medical software system for volumetric analysis of different cerebral pathologies in magnetic resonance imaging (MRI) data is presented. The software system is based on a semi-automatic segmentation algorithm and helps to overcome the time-consuming process of volume determination during monitoring of a patient. After imaging, the parameter settings-including a seed point-are set up in the system and an automatic segmentation is performed by a novel graph-based approach. Manually reviewing the result leads to reseeding, adding seed points or an automatic surface mesh generation. The mesh is saved for monitoring the patient and for comparisons with follow-up scans. Based on the mesh, the system performs a voxelization and volume calculation, which leads to diagnosis and therefore further treatment decisions. The overall system has been tested with different cerebral pathologies-glioblastoma multiforme, pituitary adenomas and cerebral aneurysms- and evaluated against manual expert segmentations using the Dice Similarity Coefficient (DSC). Additionally, intra-physician segmentations have been performed to provide a quality measure for the presented system.
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Jan Egger receives funding from BioTechMed-Graz ( " Hardware accelerated intelligent medical imaging " )
  • Dr
  • Dr
Dr. Dr. Jan Egger receives funding from BioTechMed-Graz ( " Hardware accelerated intelligent medical imaging " ).