Fully automatic multi‐organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks

• Published in: Medical physics (Lancaster) 2018-10, Vol.45 (10), p.4558-4567
• Main Authors: Tong, Nuo, Gou, Shuiping, Yang, Shuyuan, Ruan, Dan, Sheng, Ke
• Format: Article
• Language: English
• Subjects: Automation; fully convolutional neural network; head and neck cancer; Head and Neck Neoplasms - diagnostic imaging; Head and Neck Neoplasms - radiotherapy; Humans; Image Processing, Computer-Assisted - methods; image segmentation; Models, Theoretical; Neural Networks, Computer; Organs at Risk - radiation effects; shape representation model; Tomography, X-Ray Computed
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1002/mp.13147

Purpose Intensity modulated radiation therapy (IMRT) is commonly employed for treating head and neck (H&N) cancer with uniform tumor dose and conformal critical organ sparing. Accurate delineation of organs‐at‐risk (OARs) on H&N CT images is thus essential to treatment quality. Manual contouring used in current clinical practice is tedious, time‐consuming, and can produce inconsistent results. Existing automated segmentation methods are challenged by the substantial inter‐patient anatomical variation and low CT soft tissue contrast. To overcome the challenges, we developed a novel automated H&N OARs segmentation method that combines a fully convolutional neural network (FCNN) with a shape representation model (SRM). Methods Based on manually segmented H&N CT, the SRM and FCNN were trained in two steps: (a) SRM learned the latent shape representation of H&N OARs from the training dataset; (b) the pre‐trained SRM with fixed parameters were used to constrain the FCNN training. The combined segmentation network was then used to delineate nine OARs including the brainstem, optic chiasm, mandible, optical nerves, parotids, and submandibular glands on unseen H&N CT images. Twenty‐two and 10 H&N CT scans provided by the Public Domain Database for Computational Anatomy (PDDCA) were utilized for training and validation, respectively. Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), average surface distance (ASD), and 95% maximum surface distance (95%SD) were calculated to quantitatively evaluate the segmentation accuracy of the proposed method. The proposed method was compared with an active appearance model that won the 2015 MICCAI H&N Segmentation Grand Challenge based on the same dataset, an atlas method and a deep learning method based on different patient datasets. Results An average DSC = 0.870 (brainstem), DSC = 0.583 (optic chiasm), DSC = 0.937 (mandible), DSC = 0.653 (left optic nerve), DSC = 0.689 (right optic nerve), DSC = 0.835 (left parotid), DSC = 0.832 (right parotid), DSC = 0.755 (left submandibular), and DSC = 0.813 (right submandibular) were achieved. The segmentation results are consistently superior to the results of atlas and statistical shape based methods as well as a patch‐wise convolutional neural network method. Once the networks are trained off‐line, the average time to segment all 9 OARs for an unseen CT scan is 9.5 s. Conclusion Experiments on clinical datasets of H&N patients demonstrated the effect