Residual mask scoring regional convolutional neural network for multi-organ segmentation in head-and-neck CT

Abstract

The delineation of target and organs-at-risk (OARs) is a necessary step in radiotherapy treatment planning. The accuracy of the target and OAR contours directly affects the quality of radiotherapy plans. Manual contouring of OARs is the routine procedure at present, which, however, is very time-consuming and requires significant expertise, especially for those head-and-neck (HN) cancer cases, where OARs densely distribute around tumors with complex anatomical structures. In this study, we propose a deep learning-based fully automated delineation method, namely, mask scoring regional convolutional neural network (MS-RCNN), to obtain consistent and reliable OAR contours in HN CT. In the model, MR images were synthesized by a cycle-consistent generative adversarial network given CT images. A backbone network was utilized to extract features from MRI and CT independently. The high bony-structure contrast in CT and soft-tissue contrast in MRI are complementary in nature. Through combining those complementary contrasts, the accuracy of OAR delineation is expected to be improved. Due to the ability of various object detection and classification, ResNet 101 was used as backbone in MS-RCNN. Quantities including Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), mean surface distance (MSD), and residual mean square distance (RMS) were calculated to evaluate the performance of the proposed method. An average DSC, HD95, MSD and RMS of 0.78 (0.58 - 0.89), 4.88 mm (2.79 mm - 7.46 mm), 1.39 mm (0.69 mm - 1.99 mm), and 2.23 mm (1.30 mm - 3.23 mm), were respectively achieved across all of the 12 OARs by our proposed method. The proposed method is promising in facilitating auto-contouring for radiotherapy treatment planning.