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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

This image shows overall three screenshots of an interactive segmentation result for a postinterventional CT acquisition: a screenshot of an axial plane on the left side and two 3D screenshots on the next two images to the right.The red dots display the segmentation result in the two images on the left, besides the axial plane contains the user-defined seed point (blue) where the interactive segmentation has been stopped. Finally, the rightmost screenshot includes a closed surface (green) of the interactive segmentation result of the ablation zone, which has been generated from the red dots shown in the middle screenshot. From the closed surface on the other hand, a solid mask can be generated, which is used to determine the Dice Similarity Coefficient (DSC) if compared with a pure manual slice-by-slice expert volumetry. Note: for the native scan please see Fig. 2.
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f3: This image shows overall three screenshots of an interactive segmentation result for a postinterventional CT acquisition: a screenshot of an axial plane on the left side and two 3D screenshots on the next two images to the right.The red dots display the segmentation result in the two images on the left, besides the axial plane contains the user-defined seed point (blue) where the interactive segmentation has been stopped. Finally, the rightmost screenshot includes a closed surface (green) of the interactive segmentation result of the ablation zone, which has been generated from the red dots shown in the middle screenshot. From the closed surface on the other hand, a solid mask can be generated, which is used to determine the Dice Similarity Coefficient (DSC) if compared with a pure manual slice-by-slice expert volumetry. Note: for the native scan please see Fig. 2.

Mentions: Figure 3 presents a semi-automatic segmentation result of a post-interventional ablation zone for visual inspection. As the CT data has been acquired immediately after the treatment, the needle used for the ablation is still in place and therefore visible in the scan. The left image shows the axial slice with a user-defined seed point (blue) and the red dots are the border of the segmentation in this slice. The red dots represent the last nodes of the graph that are still bound to the source s after the min-cut. The image in the middle presents the segmentation result in 3D. Again, the red dots show the last nodes of the graph which are still connected to the source after applying the min-cut. Finally, the rightmost image visualizes a closed surface (green) that has been generated from the graph’s nodes representing the segmentation result. Afterwards, this closed surface is used to generate a solid mask for the calculation of the Dice Similarity Coefficient (DSC)37 with the pure manual slice-by-slice segmentations.


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)

This image shows overall three screenshots of an interactive segmentation result for a postinterventional CT acquisition: a screenshot of an axial plane on the left side and two 3D screenshots on the next two images to the right.The red dots display the segmentation result in the two images on the left, besides the axial plane contains the user-defined seed point (blue) where the interactive segmentation has been stopped. Finally, the rightmost screenshot includes a closed surface (green) of the interactive segmentation result of the ablation zone, which has been generated from the red dots shown in the middle screenshot. From the closed surface on the other hand, a solid mask can be generated, which is used to determine the Dice Similarity Coefficient (DSC) if compared with a pure manual slice-by-slice expert volumetry. Note: for the native scan please see Fig. 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3: This image shows overall three screenshots of an interactive segmentation result for a postinterventional CT acquisition: a screenshot of an axial plane on the left side and two 3D screenshots on the next two images to the right.The red dots display the segmentation result in the two images on the left, besides the axial plane contains the user-defined seed point (blue) where the interactive segmentation has been stopped. Finally, the rightmost screenshot includes a closed surface (green) of the interactive segmentation result of the ablation zone, which has been generated from the red dots shown in the middle screenshot. From the closed surface on the other hand, a solid mask can be generated, which is used to determine the Dice Similarity Coefficient (DSC) if compared with a pure manual slice-by-slice expert volumetry. Note: for the native scan please see Fig. 2.
Mentions: Figure 3 presents a semi-automatic segmentation result of a post-interventional ablation zone for visual inspection. As the CT data has been acquired immediately after the treatment, the needle used for the ablation is still in place and therefore visible in the scan. The left image shows the axial slice with a user-defined seed point (blue) and the red dots are the border of the segmentation in this slice. The red dots represent the last nodes of the graph that are still bound to the source s after the min-cut. The image in the middle presents the segmentation result in 3D. Again, the red dots show the last nodes of the graph which are still connected to the source after applying the min-cut. Finally, the rightmost image visualizes a closed surface (green) that has been generated from the graph’s nodes representing the segmentation result. Afterwards, this closed surface is used to generate a solid mask for the calculation of the Dice Similarity Coefficient (DSC)37 with the pure manual slice-by-slice segmentations.

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