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

Kainz, Bernhard; Voglreiter, Philip; Sereinigg, Michael; Wiederstein-Grasser, Iris; Mayrhauser, Ursula; Köstenbauer, Sonja; Pollari, Mika; Khlebnikov, Rostislav; Seise, Matthias; Alhonnoro, Tuomas; Häme, Yrjö; Seider, Daniel; Flanagan, Ronan; Bost, Claire; Mühl, Judith; O'Neill, David; Peng, Tingying; Payne, Stephen J.; Rueckert, Daniel; Schmalstieg, Dieter; Moche, Michael; Kolesnik, Marina; Stiegler, P.; Portugaller, Rupert Horst

doi:10.1109/isbi.2014.6867814

Cited by 5 publications

(4 citation statements)

References 8 publications

(8 reference statements)

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“…However, after carrying out several studies with a typical stroke-based approach 9 10 34 , it is clear that they would only accept such a course of action (the initialization) if the segmentation outcome is afterwards always satisfying (note: for the automatic segmentation the participating physicians had only to mark parts of the fore- and background with a simple brush; no other settings or parameters had to be defined). However, a long-term end user study regarding the presented approach already has been started within two European funded projects ClinicIMPPACT ( www.clinicimppact.eu/ ) and GoSmart ( www.gosmart-project.eu ), where post-interventional radiofrequency ablation (RFA) zones are segmented 35 .…”

Section: Discussionmentioning

confidence: 99%

Refinement-Cut: User-Guided Segmentation Algorithm for Translational Science

Egger

2014

Sci Rep

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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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Section: Discussionmentioning

confidence: 99%

Refinement-Cut: User-Guided Segmentation Algorithm for Translational Science

Egger

2014

Sci Rep

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“…For our experimentation we focus on two tasks, segmentation and volumetry. Two datasets have been used: 60 abdominal CT images of healthy human patients (Exp1 ), and 57 CT porcine livers [10] (Exp2 ). Both datasets are resampled to isotropic spacing of 1mm × 1mm × 1mm.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Exp2 : Fine Structures In order to test for limitations and to evaluate the network's ability to segment fine structures we aim to segment the ribcage with the publicly available porcine CT dataset [10]. Porcine ribs are smaller and finer than human ribs and they project largely on different anatomy (stomach and liver).…”

Section: Methodsmentioning

confidence: 99%

3D Probabilistic Segmentation and Volumetry from 2D projection images

Vlontzos¹,

Budd²,

Hou³

et al. 2020

Preprint

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X-Ray imaging is quick, cheap and useful for front-line care assessment and intra-operative real-time imaging (e.g., C-Arm Fluoroscopy). However, it suffers from projective information loss and lacks vital volumetric information on which many essential diagnostic biomarkers are based on. In this paper we explore probabilistic methods to reconstruct 3D volumetric images from 2D imaging modalities and measure the models' performance and confidence. We show our models' performance on large connected structures and we test for limitations regarding fine structures and image domain sensitivity. We utilize fast end-to-end training of a 2D-3D convolutional networks, evaluate our method on 117 CT scans segmenting 3D structures from digitally reconstructed radiographs (DRRs) with a Dice score of 0.91 ± 0.0013. Source code will be made available by the time of the conference.

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“…Note: since this is a European Project scheduled at least for three years until 2017, we will add data from several clinical partners around Europe. Furthermore, even more ablation data from a comprehensive pig study can be found on the webpage of the European Project GoSmart 44 : www.gosmart-project.eu .…”

Section: Methodsmentioning

confidence: 99%

Interactive Volumetry Of Liver Ablation Zones

Egger

Busse

Brandmaier

et al. 2015

Sci Rep

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

Cited by 5 publications

References 8 publications

Refinement-Cut: User-Guided Segmentation Algorithm for Translational Science

Refinement-Cut: User-Guided Segmentation Algorithm for Translational Science

3D Probabilistic Segmentation and Volumetry from 2D projection images

Interactive Volumetry Of Liver Ablation Zones

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