[1] | Bushberg JT, Boone JM. The essential physics of medical imaging. Lippincott Williams & Wilkins Publisher 2011. p163. | [2] | Chikui T, Okamura K, Tokumori K, Nakamura S, Shimizu M, Koga M, et al. Quantitative analyses of sonographic images of the parotid gland in patients with sj?grens syndrome. Ultrasound Med Biol 2006; 32:617-22. | [3] | Alimolu E, Bayraktar D, Bozkurt S, Eken K, Kabaaliolu, A, Apaydn A, et al. Follow-up versus tissue diagnosis in bi-rads category 3 solid breast lesions at us: a cost-consequence analysis. Diagn & Interv Radiol 2012; 18:3-10. | [4] | Savelonas MA, Iakovidis DK, Legakis I, Maroulis D. Active contours guided by echogenicity and texture for delineation of thyroid nodules in ultrasound images. IEEE Trans Inf Technol Biomed 2009; 13:519-27. | [5] | Iakovidis DK, Savelonas MA, Karkanis SA, Maroulis DE. A genetically optimized level set approach to segmentation of thyroid ultrasound images. Applied Intelligence 2007; 27:193-203. | [6] | Koundal D, Gupta S, Singh S. Automated delineation of thyroid nodules in ultrasound images using spatial neutrosophic clustering and level set. Applied Soft Computing 2016; 40:86-97. | [7] | Chang CY, Huang HC, Chen SJ. Automatic thyroid nodule segmentation and component analysis in ultrasound images. Biomed Eng: App, Basis and Comm 2010; 22:81-9. | [8] | Chang CY, Lei YF, Tseng CH, Shih SR. Thyroid segmentation and volume estimation in ultrasound images. IEEE Trans Biomed Eng 2010; 57:1348-57. | [9] | Selvathi D, Sharnitha V. Thyroid classification and segmentation in ultrasound images using machine learning algorithms. Thuckafay, India: IEEE International Conference on Signal Processing, Communication, Computing and Networking Technologies; 2011.p 836-41. | [10] | Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 2012; 1:1097-1105. | [11] | Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. Columbus, OH, USA : IEEE Conference on Computer Vision and Pattern Recognition; 2014. 580-587. | [12] | Liu Z, Li X, Luo P, Loy CC, Tang X. Semantic image segmentation via deep parsing network. Santiago, Chile: IEEE International Conference on Computer Vision; 2016. 1377-1385. | [13] | Long J., Shelhamer E., Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 2014; 39:640-51. | [14] | Zheng S, Jayasumana S, Romera-Paredes B, Vineet V, Su Z, Du D, et al. Conditional random fields as recurrent neural networks. Santiago, Chile: IEEE International Conference on Computer Vision (ICCV); 2015.p 1529-1537. | [15] | Ciresan D, Giusti A, Gambardella LM, Schmidhuber J. Deep neural networks segment neuronal membranes in electron microscopy images. Advances in neural information processing systems 2012; 25:2843-51. | [16] | Ciresan DC, Giusti A, Gambardella LM, Schmidhuber J. Mitosis detection in breast cancer histology images with deep neural networks. Med Image Comput Comput Assist Interv 2013; 16:411-8. | [17] | Wang L, Shi F, Gao Y, Li G, Gilmore JH, Lin W, et al. Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain mr image segmentation. NeuroImage 2014; 89:152-64. | [18] | Zhang W, Li R, Deng H, Wang L, Lin W, Ji S, et al. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 2015; 108:214-24. | [19] | Ravishankar H, Prabhu SM, Vaidya V, Singhal N. Hybrid approach for automatic segmentation of fetal abdomen from ultrasound images using deep learning. Prague, Czech Republic:IEEE International Symposium on Biomedical Imaging 2016.p 779-782. | [20] | Ma J, Wu F, Jiang T, Zhu J, Kong D. Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images. Med Phys 2017; 44:1678-91. | [21] | Ma J, Wu F, Zhao Q, Kong D. Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks. Int J Comput Assist Radiol Surg 2017; 12:1895-1910. | [22] | He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Santiago, Chile: IEEE International Conference on Computer Vision (ICCV); 2015.p 1026-1034. | [23] | Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R et al. Caffe: Convolutional Architecture for Fast Feature Embedding [accessed 16 May 2018 ]. | [24] | Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. Springer: European Conference on Computer Vision- ECCV 2014; 2014. p 818-33. | [25] | Simonyan K, Zisserman A. Very deep convolutional networks for largescale image recognition. [accessed 2014 . | [26] | He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Las Vegas, NV, United States: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016. p 770-778. |
|