J Li*, R Anne, Thomas Jefferson University, Philadelphia, PA

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  J Li

PO-GePV-T-47 (Sunday, 7/10/2022)   [Eastern Time (GMT-4)]

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Purpose: In resin Yttrium-90 (Y-90) selective internal radiation therapy (SIRT), liver volume sizes are needed in Y-90 activity calculations using the body-surface-area method, which are obtained from contours delineated by physicians in 3D images. MR images are usually used. The aim was to explore the feasibility of applying an Atlas-based auto-segmentation tool, MIM Atlas Segment, for liver segmentation in MR images for Y-90 SIRT.

Methods: MR images of 41 liver patients treated with resin Y-90 selective internal radiation therapy were selected for the study. 20 patients’ images were used to create an Atlas in MIM software (version 6.67), and the other 21 patients’ images were used to test the auto-segmentation. Liver contours generated with MIM Atlas Segment were compared with physician manually-delineated contours, which were taken as the standard. Dice similarity coefficient (DSC), mean distance to agreement (MDA), and ratio of volume (RV) were calculated. The auto-segmented liver contours were further evaluated with Y-90 activity calculations. Ratio of activity (RA, ratio of activity calculated using an auto-segmented liver contour to the accurate activity calculated using a manually-delineated contour), which reflected the deviation from the accurate activity, was assessed.

Results: DSCs were 0.80±0.09, MDAs were 0.68±0.31 cm, and RVs were 0.95±0.16, respectively. RAs were 1.00±0.01, which showed that the activities calculated using the auto-segmented liver contours were close to the accurate activities.

Conclusion: The auto-segmentation can be applied to generate initial liver contours for resin Y-90 SIRT. To ensure accurate activity calculations, the contours need to be reviewed and edited by physicians. The application will save contour delineation time and improve the workflow efficiency in resin Y-90 SIRT.

Keywords

Segmentation, MRI, Radiation Therapy

Taxonomy

IM/TH- image Segmentation: MRI

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