Automatic segmentation of head-neck organs by Multi-mode CNNs for radiation therapy

Objective: Accurate and fast automatic segmentation of organs is a key step for efficient planning for radiation therapy. In this paper, we propose the Multi-mode CNNs, which is for accurate and fast automatic segmentation of mandible, parotid glands, brainstem, optic nerves, cerebellum, eyes, lens, pituitary, thyroid, temporal lobes, brain and head respectively in 3D CT image of head and neck. Methods: The proposed Multi-mode CNNs consist of three convolutional neural networks. The first CNN is a simple classification network to distinguish slices of head and neck. It accelerates the next step by reducing the search space. The second network is a 3DCNN for locating the region of interest (ROI) of organs. It can enhance the robustness of the algorithm to CT images from different hospitals. The third network is a full convolution network (FCN) based on U-Net. This is a pixel-wise detailed segmented network to classify voxels in a region of interest and generate segmentation results. We used public and clinical datasets to evaluate our algorithm. The public datasets are from the MICCAI 2015 Head and Neck Auto Segmentation Grand Challenge. For the private datasets, we collected 3D CT images of 88 patients from A and B Hospital. Results: For the public datasets, our segmentation results of the nine organs in the challenge surpassed the first rank. For the private datasets, clinical trials have proved that it is effective for real image data in different hospitals. The average total segmentation time for one patient with above whole organs is less than one minute, which indicates proposed method having clinical value. Conclusion: The proposed Multi-mode CNNs can be used to accurate and fast automatic segmentation of organs in radiation therapy. It can assist doctors in outlining the organs during the planning of radiation therapy in the clinic.