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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 screenshots present a direct comparison between a pure manual segmentation (green) and a semi-automatic/interactive segmentation (red).Therefore, the three-dimensional masks of both segmentations (manual/interactive) have been merged and placed within the original dataset at the location of the ablation zone (upper left window). Easily recognizable is the bright stick pointing to the masks, which is the shaft of the RFA needle. The remaining three windows show the planes where the user-defined seed point (yellow cross) has been placed for interactive segmentation result, with the axial plane in the upper right windows, the sagittal plane in the lower left window and the coronal plane in the lower right window. Note: for the native scan please see Fig. 2.
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f4: This screenshots present a direct comparison between a pure manual segmentation (green) and a semi-automatic/interactive segmentation (red).Therefore, the three-dimensional masks of both segmentations (manual/interactive) have been merged and placed within the original dataset at the location of the ablation zone (upper left window). Easily recognizable is the bright stick pointing to the masks, which is the shaft of the RFA needle. The remaining three windows show the planes where the user-defined seed point (yellow cross) has been placed for interactive segmentation result, with the axial plane in the upper right windows, the sagittal plane in the lower left window and the coronal plane in the lower right window. Note: for the native scan please see Fig. 2.

Mentions: Figure 4 displays a comparison of a manual (green) and an automatic (red) segmentation for visual inspection. The upper left window shows both segmentation results as 3D masks superimposed on the original dataset. The upper right, lower left and lower right windows present direct comparisons between the manual and the automatic segmentations on an axial, sagittal and coronal slice, respectively. The yellow cross points to the location of the manually placed seed point for the graph construction. The lower left window shows that the algorithm tends to an over segmentation compared to the manual counterpart. However, changing the contrast window clearly shows the reason: the algorithm adapts to the bright border around the ablation zone (Fig. 5).


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 screenshots present a direct comparison between a pure manual segmentation (green) and a semi-automatic/interactive segmentation (red).Therefore, the three-dimensional masks of both segmentations (manual/interactive) have been merged and placed within the original dataset at the location of the ablation zone (upper left window). Easily recognizable is the bright stick pointing to the masks, which is the shaft of the RFA needle. The remaining three windows show the planes where the user-defined seed point (yellow cross) has been placed for interactive segmentation result, with the axial plane in the upper right windows, the sagittal plane in the lower left window and the coronal plane in the lower right window. 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

f4: This screenshots present a direct comparison between a pure manual segmentation (green) and a semi-automatic/interactive segmentation (red).Therefore, the three-dimensional masks of both segmentations (manual/interactive) have been merged and placed within the original dataset at the location of the ablation zone (upper left window). Easily recognizable is the bright stick pointing to the masks, which is the shaft of the RFA needle. The remaining three windows show the planes where the user-defined seed point (yellow cross) has been placed for interactive segmentation result, with the axial plane in the upper right windows, the sagittal plane in the lower left window and the coronal plane in the lower right window. Note: for the native scan please see Fig. 2.
Mentions: Figure 4 displays a comparison of a manual (green) and an automatic (red) segmentation for visual inspection. The upper left window shows both segmentation results as 3D masks superimposed on the original dataset. The upper right, lower left and lower right windows present direct comparisons between the manual and the automatic segmentations on an axial, sagittal and coronal slice, respectively. The yellow cross points to the location of the manually placed seed point for the graph construction. The lower left window shows that the algorithm tends to an over segmentation compared to the manual counterpart. However, changing the contrast window clearly shows the reason: the algorithm adapts to the bright border around the ablation zone (Fig. 5).

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