Fig. 2. Schematic representations of RTP-Net for fast and accurate delineation of organs-at-risk (OARs) and tumors.

a Coarse-to-fine framework with multi-resolutions for fast segmentation. A coarse-resolution model is to localize the region of interest (ROI) in the original image (labeled in the red box), and a fine-resolution model is to refine the detailed boundaries of ROI. b Adaptive VB-Net for multi-sized OAR segmentation, which can be also applied to large organs. This is achieved by adding a stridden convolution layer with a stride of 2 (Conv-s2) and a transposed convolution layer with a stride of 2 (T-Conv-s2) to the beginning and the end of the VB-Net, respectively. c Attention mechanisms used in the segmentation framework for accurate target volume delineation. The OAR-aware attention map is generated by the fine-level OAR segmentation, and the boundary-aware attention map is generated by the coarse-level target volume bounding box. Two attention maps combined with multi-dimensional adaptive loss function are adopted to modify the fine-level model for obtaining accurate target delineation.