Residual U-Net approach for thyroid nodule detection and classification from thyroid ultrasound images

References

• Gaitini D, Evans RM, Ivanac G. Chapter 16: thyroid ultrasound. EFSUMB Course Book. 2011.
   Google Scholar
• Kwak JY, Han KH, Yoon JH, et al. Thyroid imaging reporting and data system for us features of nodules: a step in establishing better stratification of cancer risk. Radiology. 2011;260(3):892–899. doi:10.1148/radiol.11110206
   PubMed Web of Science ®Google Scholar
• Lingam RK, Qarib MH, Tolley NS. Evaluating thyroid nodules: predicting and selecting malignant nodules for fine-needle aspiration (fna) cytology. Insights Imaging. 2013;4(5):617–624. doi:10.1007/s13244-013-0256-6
   PubMedGoogle Scholar
• Wang Y, Yue W, Li X, et al. Comparison study of radiomics and deep learning-based methods for thyroid nodules classification using ultrasound images. Ieee Access. 2020;8:52010–52017. doi:10.1109/ACCESS.2020.2980290
   Web of Science ®Google Scholar
• Nguyen DT, Pham TD, Batchuluun G, et al. Artificial intelligence-based thyroid nodule classification using information from spatial and frequency domains. J Clin Med. 2019;8(11):1976. doi:10.3390/jcm8111976
   PubMed Web of Science ®Google Scholar
• Kwon SW, Choi IJ, Kang JY, et al. Ultrasonographic thyroid nodule classification using a deep convolutional neural network with surgical pathology. J Digit Imaging. 2020;33(5):1202–1208. doi:10.1007/s10278-020-00362-w
   PubMed Web of Science ®Google Scholar
• Gomes Ataide EJ, Ponugoti N, Illanes A, et al. Thyroid nodule classification for physician decision support using machine learning-evaluated geometric and morphological features. Sensors. 2020;20(21):6110. doi:10.3390/s20216110
   PubMed Web of Science ®Google Scholar
• Baloch ZW, LiVolsi VA. Special types of thyroid carcinoma. Histopathol. 2018;72(1):40–52. doi:10.1111/his.13348
   PubMed Web of Science ®Google Scholar
• Online source. https://my.clevelandclinic.org/health/diseases/13121-thyroid-nodule.
   Google Scholar
• Zhou S, Wu H, Gong J, et al. Mark-guided segmentation of ultrasonic thyroid nodules using deep learning. Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine; 2018, October. p. 21–26.
   Google Scholar
• Liang X, Yu J, Liao J, et al. Convolutional neural network for breast and thyroid nodules diagnosis in ultrasound imaging. BioMed Res Int. 2020;2020.
   Web of Science ®Google Scholar
• Yang W, Dong Y, Du Q, … Zia MB. Integrate domain knowledge in training multi-task cascade deep learning model for benign–malignant thyroid nodule classification on ultrasound images. Eng Appl Artif Intell. 2021;98:104064. doi:10.1016/j.engappai.2020.104064
   Web of Science ®Google Scholar
• Chu C, Zheng J, Zhou Y. Ultrasonic thyroid nodule detection method based on U-Net network. Comput Methods Programs Biomed. 2021;199:105906. doi:10.1016/j.cmpb.2020.105906
   PubMed Web of Science ®Google Scholar
• Ma J, Wu F, Jiang TA, et al. Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks. Int J Comput Assist Radiol Surg. 2017;12(11):1895–1910. doi:10.1007/s11548-017-1649-7
   PubMed Web of Science ®Google Scholar
• Pedraza L, Vargas C, Narvaez F, et al. An open access thyroid ultrasound-image database. Proceedings of the 10th International Symposium on Medical Information Processing and Analysis; 28 January 2015; Cartagena de Indias, Colombia. p. 1–6.
   Google Scholar
• Zhu Y, Fu Z, Fei J. An image augmentation method using convolutional network for thyroid nodule classification by transfer learning. Proceedings of the 3rd IEEE International Conference on Computer and Communication; 13–16 December 2017; Chengdu, China. p. 1819–1823.
   Google Scholar
• Sudarshan VK, Mookiah MRK, Acharya UR, et al. Application of wavelet techniques for cancer diagnosis using ultrasound images: a review. Comput Biol Med. 2016;69:97–111. doi:10.1016/j.compbiomed.2015.12.006
   PubMed Web of Science ®Google Scholar
• Yu Y, Acton ST. Speckle reducing anisotropic diffusion. IEEE Trans Image Process. November 2002;11(11).
   PubMed Web of Science ®Google Scholar
• Perona, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell. 1990;12:629–639. doi:10.1109/34.56205
   Web of Science ®Google Scholar
• Tsiotsios C, Petrou M. On the choice of the parameters for anisotropic diffusion in image processing. Pattern Recognit. 2012.
   Web of Science ®Google Scholar
• Tomasi C, Pepartment CS. (1998). ‘Bilateral Filtering for Gray and Color Images,’.
   Google Scholar
• Nugroho HA, Nugroho A, Choridah L. Thyroid nodule segmentation using active contour bilateral filtering on ultrasound images. 2015 International Conference on Quality in Research (QiR); 2015, August: IEEE. p. 43–46.
   Google Scholar
• Gireesha HM, Nanda S. Thyroid nodule segmentation and classification in ultrasound images. Int J Eng Res Technol. 2014.
   Google Scholar
• Song G, Xue F, Zhang C. A model using texture features to differentiate the nature of thyroid nodules on sonography. J Ultrasound Med. 2015;34(10):1753–1760. doi:10.7863/ultra.15.14.10045
   PubMed Web of Science ®Google Scholar
• Ma J, Wu F, Zhu J, et al. A pre-trained convolutional neural network based method for thyroid nodule diagnosis. Ultrasonics. 2017;73:221–230. doi:10.1016/j.ultras.2016.09.011
   PubMed Web of Science ®Google Scholar
• Pan H, Zhou Q, Latecki LJ. SGUNET: semantic guided UNET for thyroid nodule segmentation. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI); 2021, April: IEEE. p. 630–634.
   Google Scholar
• Shahroudnejad A, Vega R, Forouzandeh A, … Punithakumar K. Thyroid nodule segmentation and classification using deep convolutional neural network and rule-based classifiers. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC); 2021, November: IEEE. p. 3118–3121.
   Google Scholar