Purpose: Organ delineation is crucial to diagnosis and therapy, while is also labor-intensive and observer-dependent. Dual energy CT (DECT) provides additional image contrast than conventional single energy CT (SECT), which may facilitate automatic organ segmentation. This work aims to develop an automatic multi-organ segmentation approach using deep learning for head-and-neck region on DECT.
Methods: We proposed a Mask scoring regional convolutional neural network (R-CNN) where comprehensive features are firstly learnt from two independent pyramid networks and are then 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 avoid 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 volume of interest (VOI) and the shape of that organ’s segmentation within that VOI. 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.
Results: 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). Meanwhile, by using the DECT, the proposed method is also significantly superior to a recently developed CNN-based method in most of organs.
Conclusion: Quantitative results demonstrated the feasibility of the proposed method, the superiority of using DECT to SECT, and the advantage of the proposed R-CNN over CNN. The proposed segmentation tool has the potential to facilitate the current radiation therapy workflow in treatment planning.