Deep-Learning Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy

PURPOSE:The aim was to explore the feasibility of applying an atlas-based auto-segmentation tool, MIM Atlas Segment, for liver delineation in MR images in Y-90 selective internal radiation therapy (SIRT).MATERIALS AND METHODS:MR images of 41 liver patients treated with resin Y-90 SIRT were included: 20 patients' images were used to create an atlas, and the other 21 patients' images were used for testing. Auto-segmentation of liver in the MR images was performed with MIM Atlas Segment, and various settings for the auto-segmentation (i.e., with and without normalized deformable registration, single atlas-match and multi-atlas match, and multi-atlas match using different finalization methods) were tested. Auto-segmented liver contours were compared with physician manually-delineated contours, using Dice similarity coefficient (DSC) and mean distance to agreement (MDA). Ratio of volume (RV) and ratio of activity (RA) were calculated to further evaluate the auto-segmentation results.RESULTS:Auto-segmentations with normalized deformable registration generated better contours than those without normalized deformable registration. With normalized deformable registration, 3-atlas match using Majority Vote (MV) method generated better results than single-atlas match and 3-atlas match using STAPLE method, and generated similar results as 5-atlas match using MV method or STAPLE method. The average DSC, MDA, and RV of the contours generated with normalized deformable registration are 0.80-0.83, 0.60-0.67, and 0.91-1.00 cm, respectively. The average RA are 1.00-1.01, which indicate that the activities calculated using the auto-segmented liver contours are close to the accurate activities.CONCLUSION:The atlas-based auto-segmentation can be applied to generate initial liver contours in MR images for resin Y-90 SIRT, which can be used for activity calculations after physicians review.