Fig. 7: The performance of target volume delineation by the proposed RTP-Net, compared with U-Net, nnU-Net, and Swin UNETR.
From: Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy
a Delineation results of the clinical target volume (CTV) and planning target volume (PTV) by the proposed RTP-Net, U-Net, nnU-Net, and Swin UNETR, labeled by red color. (b) Dice coefficients and (c) inference times of four methods in target volume delineation, shown in box-and-whisker plots. The first quartile forms the bottom and the third quartile forms the top of the box, in which the line and the plus sign represent the median and the mean values, respectively. The whiskers range from minimum to maximum showing all points. Statistical analyses in (b) and (c) are performed using two-way ANOVA followed by Dunnett’s multiple comparison tests, with n = 10 replicates per condition. The two-tailed adjusted p values of Dice coefficients in (b) between RTP-Net and other three methods (U-Net, nnU-Net, and Swin UNETR) are 0.420, 0.999, and 0.166 for CTV segmentation, respectively, while 0.951, 0.859, and 0.832 for PTV segmentation, respectively. All two-tailed adjusted p values of inference times in (c) between RTP-Net and other three methods are lower than 0.001, indicated with ***. (d) Overview of the organs-at-risk (OARs) and target volumes. The segmentation results of PTV and neighboring bag bowel, vertebra, and pelvis are marked in red, green, pink, and blue, respectively.
