Multi-organ segmentation on head and neck dual-energy CT using Deep Neural Networks

This work aims to develop an automatic multi-organ segmentation approach based on deep learning for head - and- neck region on dual energy CT. The proposed method proposed a Mask scoring R-CNN where comprehensive features are first learnt from two independent pyramid networks and then are combined via deep attention strategy to highlight the informative ones extracted from both two channels of low and high energy CT. To perform multi-organ segmentation and solve the problem of misclassification, a mask scoring subnetwork was integrated into the Mask R-CNN framework to build the correlation between the class of potential detected organ’s ROI and the shape of that organ’s segmentation within that ROI. We trained and tested our model on DECT images from 66 head-and-neck cancer patients with manual contours of 19 organs as training target and ground truth. For large- and mid-sized organs such as brain and parotid, the proposed method successfully achieved average Dice similarity coefficient (DSC) larger than 0.8. For small-sized organs with very low contrast such as chiasm, cochlea, lens and optic nerves, the DSCs ranged between 0.5 and 0.8. With the propose method, using DECT images outperforms using SECT in all 19 organs with statistical significance in DSC (p<0.05). Quantitative results demonstrated the feasibility of the proposed method, the superiority of using DECT to conventional SECT, and the advantage of the proposed R-CNN over FCN. The proposed method has the potential to facilitate the current radiation therapy work flow in treatment planning.