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Interactive Volumetry Of Liver Ablation Zones.

Egger J, Busse H, Brandmaier P, Seider D, Gawlitza M, Strocka S, Voglreiter P, Dokter M, Hofmann M, Kainz B, Hann A, Chen X, Alhonnoro T, Pollari M, Schmalstieg D, Moche M - Sci Rep (2015)

Bottom Line: 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).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Neuroscience and Biomedical Engineering, Aalto University, Rakentajanaukio 2 C, 02150 Espoo, Finland.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Overall workflow of the RF ablation zone segmentation: a sphere (left) is used to construct a graph (second image from the left).The graph is constructed (not visible to the user) at the user-defined seed point position within the image (third image from the left). Finally, the segmentation result (red) corresponding to the seed point is shown to the user (rightmost image).
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f10: Overall workflow of the RF ablation zone segmentation: a sphere (left) is used to construct a graph (second image from the left).The graph is constructed (not visible to the user) at the user-defined seed point position within the image (third image from the left). Finally, the segmentation result (red) corresponding to the seed point is shown to the user (rightmost image).

Mentions: The semi-automatic segmentation algorithm uses a spherical template to set up a three-dimensional graph G(V,E) around the ablation zone45. Overall, the graph consists of nodes and edges connecting these nodes. Thereby, the nodes are sampled along rays whose origin resides at a user-defined seed point while their direction points towards the surface of a polyhedron enclosing the seed point46. In addition we use two virtual nodes: a source s and a sink t to construct the graph. After graph construction, the segmentation result is calculated by dividing the graph into two sets of nodes via a Min-Cut/Max-Flow algorithm47. As a result, one set of nodes represents the ablation zone (foreground) and is bound to one of the virtual nodes, e.g. the sink t. The other set represents the surrounding structures (background) and is bound to the other virtual node (in this case the source s). The energy function of the graph cuts follows the Gibbs model48 and the cost function relates to the publication of Li et al.49 where you need a fixed average gray value of the region to calculate the single weights. Moreover, the approach was designed to segment longish structures like the aorta5051 and needed a centerline as input, which makes it not applicable for an interactive real-time approach. In contrast, our approach needs only one seed point that can also be used to derive the average gray value on the fly during the segmentation and thus makes it gray value independent. Furthermore, this means that it can also handle cases with different average ablation values. Amongst others, the basic segmentation scheme has already been successfully applied to pituitary adenomas52 and prostate central glands (PCG)53. The underlying workflow is shown in Fig. 10 for an post-interventional CT scan of a patient whose liver tumor has been treated with an RFA: a spherical template (blue, leftmost image) has been applied to set up the three-dimensional graph G(V,E) – consisting of nodes and edges – in the second image from the left. This graph G is automatically constructed at a seed point position within the image (here indicated by several 2D slices) – note: in general the graph is not visible to the user during the segmentation process, rather it is automatically constructed in the background. In this example the RFA needle is still visible within the image (bright parts inside the liver) and especially in the second and third image the characteristic umbrella shape of the fully expanded RFA needle is noticeable. Afterwards, as above already mentioned, the graph is automatically divided by the Min-Cut/Max-Flow algorithm into two disjoint sets of nodes: one set representing the ablation zone and the other one the surrounding background. However, the transition between these two sets – or in other words the last nodes of every ray that are still bound to the user-defined seed point – is the actual segmentation result that is displayed to the user in the rightmost image of Fig. 10 (red). Additionally, note that these red dots in the rightmost image (representing the segmentation result) were actually sampled nodes during the graph construction in the beginning.


Interactive Volumetry Of Liver Ablation Zones.

Egger J, Busse H, Brandmaier P, Seider D, Gawlitza M, Strocka S, Voglreiter P, Dokter M, Hofmann M, Kainz B, Hann A, Chen X, Alhonnoro T, Pollari M, Schmalstieg D, Moche M - Sci Rep (2015)

Overall workflow of the RF ablation zone segmentation: a sphere (left) is used to construct a graph (second image from the left).The graph is constructed (not visible to the user) at the user-defined seed point position within the image (third image from the left). Finally, the segmentation result (red) corresponding to the seed point is shown to the user (rightmost image).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4612735&req=5

f10: Overall workflow of the RF ablation zone segmentation: a sphere (left) is used to construct a graph (second image from the left).The graph is constructed (not visible to the user) at the user-defined seed point position within the image (third image from the left). Finally, the segmentation result (red) corresponding to the seed point is shown to the user (rightmost image).
Mentions: The semi-automatic segmentation algorithm uses a spherical template to set up a three-dimensional graph G(V,E) around the ablation zone45. Overall, the graph consists of nodes and edges connecting these nodes. Thereby, the nodes are sampled along rays whose origin resides at a user-defined seed point while their direction points towards the surface of a polyhedron enclosing the seed point46. In addition we use two virtual nodes: a source s and a sink t to construct the graph. After graph construction, the segmentation result is calculated by dividing the graph into two sets of nodes via a Min-Cut/Max-Flow algorithm47. As a result, one set of nodes represents the ablation zone (foreground) and is bound to one of the virtual nodes, e.g. the sink t. The other set represents the surrounding structures (background) and is bound to the other virtual node (in this case the source s). The energy function of the graph cuts follows the Gibbs model48 and the cost function relates to the publication of Li et al.49 where you need a fixed average gray value of the region to calculate the single weights. Moreover, the approach was designed to segment longish structures like the aorta5051 and needed a centerline as input, which makes it not applicable for an interactive real-time approach. In contrast, our approach needs only one seed point that can also be used to derive the average gray value on the fly during the segmentation and thus makes it gray value independent. Furthermore, this means that it can also handle cases with different average ablation values. Amongst others, the basic segmentation scheme has already been successfully applied to pituitary adenomas52 and prostate central glands (PCG)53. The underlying workflow is shown in Fig. 10 for an post-interventional CT scan of a patient whose liver tumor has been treated with an RFA: a spherical template (blue, leftmost image) has been applied to set up the three-dimensional graph G(V,E) – consisting of nodes and edges – in the second image from the left. This graph G is automatically constructed at a seed point position within the image (here indicated by several 2D slices) – note: in general the graph is not visible to the user during the segmentation process, rather it is automatically constructed in the background. In this example the RFA needle is still visible within the image (bright parts inside the liver) and especially in the second and third image the characteristic umbrella shape of the fully expanded RFA needle is noticeable. Afterwards, as above already mentioned, the graph is automatically divided by the Min-Cut/Max-Flow algorithm into two disjoint sets of nodes: one set representing the ablation zone and the other one the surrounding background. However, the transition between these two sets – or in other words the last nodes of every ray that are still bound to the user-defined seed point – is the actual segmentation result that is displayed to the user in the rightmost image of Fig. 10 (red). Additionally, note that these red dots in the rightmost image (representing the segmentation result) were actually sampled nodes during the graph construction in the beginning.

Bottom Line: 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).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Neuroscience and Biomedical Engineering, Aalto University, Rakentajanaukio 2 C, 02150 Espoo, Finland.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus