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

Abstract

The aim was to evaluate a deep learning-based auto-segmentation method for liver delineation in Y-90 selective internal radiation therapy (SIRT). A deep learning (DL)-based liver segmentation model using the U-Net3D architecture was built. Auto-segmentation of the liver was tested in CT images of SIRT patients. DL auto-segmented liver contours were evaluated against physician manually-delineated contours. Dice similarity coefficient (DSC) and mean distance to agreement (MDA) were calculated. The DL-model-generated contours were compared with the contours generated using an Atlas-based method. Ratio of volume (RV, the ratio of DL-model auto-segmented liver volume to manually-delineated liver volume), and ratio of activity (RA, the ratio of Y-90 activity calculated using a DL-model auto-segmented liver volume to Y-90 activity calculated using a manually-delineated liver volume), were assessed. Compared with the contours generated with the Atlas method, the contours generated with the DL model had better agreement with the manually-delineated contours, which had larger DSCs (average: 0.94 ± 0.01 vs 0.83 ± 0.10) and smaller MDAs (average: 1.8 ± 0.4 mm vs 7.1 ± 5.1 mm). The average RV and average RA calculated using the DL-model-generated volumes are 0.99 ± 0.03 and 1.00 ± 0.00, respectively. The DL segmentation model was able to identify and segment livers in the CT images and provide reliable results. It outperformed the Atlas method. The model can be applied for SIRT procedures.