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

Abstract

In resin Yttrium-90 (Y-90) selective internal radiation therapy (SIRT), liver volume sizes are needed for Y-90 activity calculations using the body-surface-area method, which are obtained from contours that are manually delineated in 3D images. The aim was to apply a deep-learning based auto-segmentation method for liver delineation for Y-90 SIRT. A deep-learning-based liver segmentation method was applied using the U-Net3D architecture, which is a 3D convolutional neural network (CNN) extended from the original 2D U-Net architecture for 3D objects in medical imaging. The segmentation model was trained on the Liver Tumor Segmentation (LiTS) dataset. The training data set contained 130 CT scans, and the test data set contained 70 CT scans. The model was deployed in the clinic using DICOM communication. Auto-segmentation of liver in the CT images of 18 SIRT patients was studied. The CT images were exported from clinical database to the segmentation model's DICOM location, where a monitoring software detected the incoming data and automatically ran the liver segmentation. The results were then returned to the original DICOM location where the CT images were stored. Auto-segmented liver contours were compared with physician manually-delineated contours. Dice similarity coefficient (DSC), mean distance to agreement (MDA), ratio of volume (RV), and ratio of activity (RA, ratio of activity calculated using an auto-segmented liver contour to the accurate activity calculated using a manually-delineated contour), were assessed. DSC, MDA, and RV are 0.942±0.014 (range: 0.908-0.959), 1.902±0.503 mm (range: 1.043-2.956 mm), and 0.988±0.039 (range: 0.901-1.045), respectively. RA is 1.001±0.003 (range: 0.993-1.007), which indicates that the activities calculated using the auto-segmented liver contours are close to the accurate activities. The segmentation model was able to successfully identify and segment livers in the CT images, and provide accurate and reliable results. The proposed method is beneficial for clinical use as it can process large amounts of data quickly and efficiently, and can be easily deployed in a clinical environment using DICOM communication.