|
|
|
| Segmentation of Organs at Risk on Head and Neck CT for Radiotherapy Based on 3D Deep Residual Fully Convolutional Neural Network |
| Tian Juanxiu1,2, Liu Guocai1,4*, Gu Shanshan3, Gu Dongdong1, Gong Junhui1,2 |
1(College of Electrical and Information Engineering, Hunan University, Changsha 410082, China);
2(College of Computer and Communication, Hunan Institute of Engineering, Xiangtan 411104, Hunan, China);
3(Departments of Radiation Oncology, Chinese PLA General Hospital, Beijing 100853, China);
4(National Engineering Laboratory for Robot Visual Perception and Control Technology, Changsha 410082, China) |
|
|
|
|
Abstract Segmentation of organs at risk (OARs) is a crucial process during the planning of radiation therapy for head and neck cancer treatment. However, accurate OAR segmentation in CT images is a challenging task. Manual delineation of OARs is tedious, time-consuming and inconsistent. To tackle these challenges, we proposed an automatic deep-learning-based method for head and neck OARs segmentation. A modified V-Net structure was constructed to extract deep and shallow features of OARs by specialized end-to-end supervised learning. To address the extremely class imbalances of small organs, a positional prior knowledge restricted sampling strategy was proposed, and Dice loss function was used to train the network. The strategy could not only accelerate the training process and improve the segmentation performance, but also ensure the accuracy of small organ segmentation. The performance of the proposed method was validated on PDDCA dataset, which was used in Head and Neck Auto-Segmentation Challenge of MICCAI 2015. The mean Dice coefficient of each organ was 0.945 of mandible, 0.884 of left parotid gland, 0.882 of right parotid gland, 0.863 of brainstem, 0.825 of leftsubmandibular gland, 0.842 of rightsubmandibular gland, 0.807 of left optic nerve, 0.847 of right optic nerve and 0.583 of optic chiasm. The 95% of Hausdorff distances of mandible, parotid glands, brainstem and submandibular glands was all within 3 mm. The contour mean distance of all organs was less than 1.2 mm. The experimental results demonstrated that the performance of the proposed method was superior to the compared state-of-the-art algorithms on segmentation of most of OARs.
|
|
Received: 11 September 2018
|
|
|
|
|
|
[1] Torre LA, Bray F, Siegel RL, et al. Global cancer statistics 2012[J]. CA: A Cancer Journal for Clinicians, 2015, 65:87-108.
[2] Ibragimov B, Xing Lei. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks [J]. Medical Physics, 2017, 44(2):547-557.
[3] Dobbs HJ, Parker RP. The respective roles of the simulator and computed tomography in radiotherapy planning: a review [J]. Clinical Radiology, 1984, 35(6): 433-439.
[4] Raudaschl PF, Zaffino P, Sharp GC, et al. Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015 [J]. Medical Physics, 2017, 44(5):2020-2036.
[5] 段丁娜, 张欢, 邱陈辉, 等. 基于活动轮廓模型的图像分割算法综述[J]. 中国生物医学工程学报,2015, 34(4): 445-454.
[6] Rueckert D, Glocker B, Kainz B. Learning clinically useful information from images: Past, present and future [J]. Medical Image Analysis, 2016, 33:13-18.
[7] Wang Zhensong, Wei Lifang, Wang Li, et al. Hierarchical vertex regression-based segmentation of head and neck CT images for radiotherapy planning [J]. IEEE Transactions on Image Processing, 2018, 27(2):923-937.
[8] Saito A, Nawano S, Shimizu A. Joint optimization of segmentation and shape prior from level-set-based statistical shape model, and its application to the automated segmentation of abdominal organs [J]. Medical Image Analysis, 2016, 28(33):46-65.
[9] Astaraki M, Severgnini M, Milan V, et al. Evaluation of localized region-based segmentation algorithms for CT-based delineation of organs at risk in radiotherapy [J]. Physics & Imaging in Radiation Oncology, 2018, 5:52-57.
[10] Mannion-Haworth R, Bowes M, Ashman A, et al. Fully automatic segmentation of head and neck organs using active appearance models [EB/OL]. http://midasjournal.org/browse/publication/967, 2016-01-29/2018-09-09.
[11] Iglesias JE, Sabuncu MR. Multi-atlas segmentation of biomedical images: a survey [J]. Medical Image Analysis, 2015, 24 (1): 205-219.
[12] Walker GV, Awan M, Tao R, et al. Prospective randomized double-blind study of atlas-based organ-at-risk auto segmentation-assisted radiation planning in head and neck cancer[J]. Radiotherapy & Oncology, 2014, 112(3):321-325.
[13] Dean JA, Welsh LC, Mcquaid D, et al. Assessment of fully-automated atlas-based segmentation of novel oral mucosal surface organ-at-risk [J]. Radiotherapy & Oncology, 2016, 119(1):166-171.
[14] 田娟秀, 刘国才, 谷珊珊,等. 医学图像分析深度学习方法研究与挑战[J]. 自动化学报, 2018, 44(3): 401-424.
[15] Farabet C, Couprie C, Najman L, et al. Learning hierarchical features for scene labeling [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8):1915-1929.
[16] He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916.
[17] He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition [C] // Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
[18] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition [EB/OL]. https://arxiv.org/abs/1409.1556, 2015-05-10/2018-09-09.
[19] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation [C] // Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 3431-3440.
[20] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation [C] // Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015. Cham: Springer, 2015: 234-241.
[21] Milletari F, Navab N, Ahmadi S A. V-net: fully convolutional neural networks for volumetric medical image segmentation [C] // Fourth International Conference on 3D Vision. Stanford: IEEE, 2016: 565-571.
[22] PDDCA. A Public Domain Database for Computational Anatomy [EB/OL]. http://www.imagenglab.com/newsite/pddca/, 2015-12-01/2018-09-09.
[23] Brouwer CL, Steenbakkers RJ, Bourhis J, et al. CT-based delineation of organs at risk in the head and neck region: DAHANCA,EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology and TROG consensus guidelines [J]. Radiotherapy & Oncology, 2015, 117(1):83-90.
[24] Nyúl LG, Udupa JK, Zhang X. New variants of a method of MRI scale standardization [J]. IEEE Transactions on Medical Imaging, 2000, 19(2):143-150.
[25] Zhu Wentao, Huang Yufang, Tang Hui et al. AnatomyNet: Deep 3D Squeeze-and-excitation U-Nets for fast and fully automated whole-volume anatomical segmentation [EB/OL]. https://arxiv.org/abs/1808.05238v1, 2018-08-15/2018-12-12. |
| [1] |
Yu Hui, Wang Shuo, Li Xinrui, Deng Chenyang, Sun Jinglai, Zhang Lixin, Cao Yuzhen. Algorithm Study of Real-Time Detection of Sleep Apnea-Hypopnea Event Based on Long-Short Term Memory-Convolutional Neural Network[J]. Chinese Journal of Biomedical Engineering, 2020, 39(3): 303-310. |
| [2] |
Song Shangling, Yang Yang, Li Xia, Feng Hao. Pulmonary Nodule Detection Algorithm Based on Faster-RCNN[J]. Chinese Journal of Biomedical Engineering, 2020, 39(2): 129-136. |
|
|
|
|
