Skip to main content
SpringerLink
Log in

Assessment of encoder-decoder-based segmentation models for thyroid ultrasound images

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Encoder-decoder-based semantic segmentation models classify image pixels into the corresponding class, such as the ROI (region of interest) or background. In the present study, simple / dilated convolution / series / directed acyclic graph (DAG)-based encoder-decoder semantic segmentation models have been implemented, i.e., SegNet (VGG16), SegNet (VGG19), U-Net, mobileNetv2, ResNet18, ResNet50, Xception and Inception networks for the segment TTUS(Thyroid Tumor Ultrasound) images. Transfer learning has been used to train these segmentation networks using original and despeckled TTUS images. The performance of the networks has been calculated using mIoU and mDC metrics. Based on the exhaustive experiments, it has been observed that ResNet50-based segmentation model obtained the best results objectively with values 0.87 for mIoU, 0.94 for mDC, and also according to radiologist opinion on shape, margin, and echogenicity characteristics of segmented lesions. It is noted that the segmentation model, namely ResNet50, provides better segmentation based on objective and subjective assessment. It may be used in the healthcare system to identify thyroid nodules accurately in real time.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31

Similar content being viewed by others

Abbreviations

ROI:

Region of interest

MRI:

Magnetic resonance imaging

CT scan:

Computerized tomography

US:

Ultrasound

DAG:

Directed acyclic graph

DL:

Deep learning

SSD:

Single-shot detector

YOLO version-X:

You Only Look Once

R-CNN:

Region-CNN

mIoU:

Mean interaction over union

mDC:

Dice coefficient

TTUS:

Thyroid tumor US

CAD:

Computer-aided diagnosis

References 

  1. Ahmed J (2016) TDTD : thyroid disease type diagnostics. Int Conf Intell Syst Eng 2016:1–7. https://doi.org/10.1109/INTELSE.2016.7475160

    Article  Google Scholar 

  2. Gesing A (2015) The thyroid gland and the process of aging;; what is new? Thyroid Res 8:A8. https://doi.org/10.1186/1756-6614-8-S1-A8

    Article  PubMed Central  Google Scholar 

  3. Nagataki S, Nyström E (2002) Epidemiology and Primary Prevention of Thyroid Cancer. Thyroid 12:889–896. https://doi.org/10.1089/105072502761016511

    Article  PubMed  Google Scholar 

  4. Papini E, Monpeyssen H, Frasoldati A, Hegedüs L (2020) 2020 European Thyroid Association Clinical Practice Guideline for the Use of Image-Guided Ablation in Benign Thyroid Nodules. Eur Thyroid J 9:172–185. https://doi.org/10.1159/000508484

    Article  PubMed  PubMed Central  Google Scholar 

  5. Deng YJ, Li HT, Wang M, Li N, Tian T, Wu Y, Xu P, Yang S, Zhai Z, Zhou LH, Hao Q, Song DL, Jin TB, Lyu J, Dai ZJ (2020) Global burden of thyroid cancer from 1990 to 2017. JAMA Netw open 3:e208759. https://doi.org/10.1001/jamanetworkopen.2020.8759

    Article  PubMed  PubMed Central  Google Scholar 

  6. La Vecchia C, Malvezzi M, Bosetti C, Garavello W, Bertuccio P, Levi F, Negri E (2015) Thyroid cancer mortality and incidence: a global overview. Int J Cancer 136:2187–2195. https://doi.org/10.1002/ijc.29251

    Article  CAS  PubMed  Google Scholar 

  7. Mathur P, Krishnan Sathishkumar, Chaturvedi M, Das, B-Level P, Kondalli, Sudarshan L, Santhappan S, Nallasamy V, John A, Narasimhan S, Roselind FS (2020) Cancer Statistics, 2020: report from National Cancer Registry Programme, India. JCO Glob Oncol 6:1063–1075.https://doi.org/10.1200/GO.20.00122

  8. Saraf J, Kalpana V (2017) Thyroid cancer detection using image processing. Int J Res Sci Innov IV:75–77

    Google Scholar 

  9. Chung R, Kim D (2019) Imaging of thyroid nodules. Appl Radiol 48:16–26

    Article  Google Scholar 

  10. Hoang JK, Sosa JA, Nguyen XV, Galvin PL, Oldan JD (2014) Imaging thyroid disease. Updates, imaging approach, and management pearls. Radiol Clin North Am 53:145–161. https://doi.org/10.1016/j.rcl.2014.09.002

    Article  PubMed  Google Scholar 

  11. Jaglan P, Dass R, Duhan M (2019) Breast cancer detection techniques: issues and challenges. J Inst Eng Ser B 100:379–386. https://doi.org/10.1007/s40031-019-00391-2

    Article  Google Scholar 

  12. Chaudhary V, Bano S (2013) Thyroid ultrasound. Indian J. Endocrinol Metab 17:219–227. https://doi.org/10.4103/2230-8210.109667

    Article  Google Scholar 

  13. Elangovan A, Jeyaseelan T (2016) Medical imaging modalities: a survey. Int Conf Emerg Trends Eng Technol Sci 1–4. https://doi.org/10.1109/ICETETS.2016.7603066

  14. Carson PL, Fenster A (2009) Anniversary paper: Evolution of ultrasound physics and the role of medical physicists and the AAPM and its journal in that evolution. Med Phys 36:411–428. https://doi.org/10.1118/1.2992048

    Article  PubMed  Google Scholar 

  15. Yadav N, Dass R, Virmani J (2022) Despeckling filters applied to thyroid ultrasound images : a comparative analysis. Multimed Tools Appl. https://doi.org/10.1007/s11042-022-11965-6

    Article  PubMed  PubMed Central  Google Scholar 

  16. Biradar N, Dewal ML, Rohit MK, Gowre S, Gundge Y (2016) Blind source parameters for performance evaluation of despeckling filters. Hindawi Publ Corp J Biomed Imaging 2016:1–12. https://doi.org/10.1155/2016/3636017

    Article  Google Scholar 

  17. Kriti VJ, Agarwal R (2019) Assessment of despeckle filtering algorithms for segmentation of breast tumours from ultrasound images. Biocybern Biomed Eng 39:100–121. https://doi.org/10.1016/j.bbe.2018.10.002

    Article  Google Scholar 

  18. Biradar N, Dewal ML, Rohit MK (2015) Speckle noise reduction in B-mode echocardiographic images: a comparison. IETE Tech Rev (Institution Electron Telecommun Eng India) 32:435–453. https://doi.org/10.1080/02564602.2015.1031714

    Article  Google Scholar 

  19. Koundal D, Gupta S, Singh S (2016) Speckle reduction method for thyroid ultrasound images in neutrosophic domain. IET Image Process 10:167–175. https://doi.org/10.1049/iet-ipr.2015.0231

    Article  Google Scholar 

  20. Bhosale YH, Patnaik KS (2022) Application of deep learning techniques in diagnosis of Covid-19 (coronavirus): a systematic review. Neural Process Lett 16:1–53. https://doi.org/10.1007/s11063-022-11023-0

    Article  Google Scholar 

  21. Bhosale YH, Patnaik KS (2022) IoT Deployable lightweight deep learning application for COVID-19 detection with lung diseases using RaspberryPi. Int Conf IoT Blockchain Technol. https://doi.org/10.1109/ICIBT52874.2022.9807725

    Article  Google Scholar 

  22. Lundervold AS, Lundervold A (2018) An overview of deep learning in medical imaging focusing on MRI. Z Med Phys 29(2):102–127. https://doi.org/10.1016/j.zemedi.2018.11.002

    Article  PubMed  Google Scholar 

  23. Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39:2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615

    Article  PubMed  Google Scholar 

  24. Siam M, Gamal M, Abdel-Razek M, Yogamani S, Jagersand M, Zhang H (2018) A comparative study of real-time semantic segmentation for autonomous driving. CVPR Work 700–710. https://doi.org/10.1109/CVPRW.2018.00101

  25. Yin S, Zhang Z, Li H, Peng Q, You X, Furth SL, Tasian GE, Fan Y (2019) Fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network. IEEE 16th Int Symp Biomed Imaging 1741–1744. https://doi.org/10.1109/isbi.2019.8759170

  26. Tabrizi PR, Mansoor A, Cerrolaza JJ, Jago J, Linguraru MG (2018) Automatic kidney segmentation in 3D pediatric ultrasound images using deep neural networks and weighted fuzzy active shape model. IEEE 15th Int Symp Biomed Imaging (ISBI 2018) 2018-April:1170–1173. https://doi.org/10.1109/ISBI.2018.8363779

  27. Yin S, Peng Q, Li H, Zhang Z, You X, Fischer K, Susan L (2018) Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks. Med Image Anal 60:101602. https://doi.org/10.1016/j.media.2019.101602

  28. Almajalid R, Shan J, Du Y, Zhang M (2019) Development of a deep-learning-based method for breast ultrasound image segmentation. Proc - 17th IEEE Int Conf Mach Learn Appl ICMLA 2018 1103–1108. https://doi.org/10.1109/ICMLA.2018.00179

  29. Yap M, Goyal M, Osman F, … RM-J of M, 2018 U (2018) Breast ultrasound lesions recognition: end-to-end deep learning approaches. J Med Imaging 6:1https://doi.org/10.1117/1.jmi.6.1.011007

  30. Hu Y, Guo Y, Wang Y, Yu J, Li J, Zhou S, Chang C (2019) Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model. Med Phys 46:215–228. https://doi.org/10.1002/mp.13268

    Article  PubMed  Google Scholar 

  31. Kumar V, Webb JM, Gregory A, Denis M, Meixner DD, Bayat M, Whaley DH, Fatemi M, Alizad A (2018) Automated and real-time segmentation of suspicious breast masses using convolutional neural network. PLoS One 1–18. https://doi.org/10.1371/journal.pone.0195816

  32. Xing J, Li Z, Wang B, Yu B, Zanjani FG, Zheng A, Duits R, Tan T (2019) Automated segmentation of lesions in ultrasound using semi-pixel-wise cycle generative adversarial nets. preprint( arXiv:1905.01902v1) 1–10

  33. Cao Z, Duan L, Yang G, Yue T, Chen Q (2019) An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures. BMC Med Imaging 19:1–9. https://doi.org/10.1186/s12880-019-0349-x

    Article  Google Scholar 

  34. Xie Y, Chen K, Lin J (2017) An automatic localization algorithm for ultrasound breast tumors based on human visual mechanism. Sensors (Switzerland) 17:1–15. https://doi.org/10.3390/s17051101

    Article  Google Scholar 

  35. Xu Y, Wang Y, Yuan J, Cheng Q, Wang X, Carson PL (2019) Medical breast ultrasound image segmentation by machine learning. Ultrasonics 91:1–9. https://doi.org/10.1016/j.ultras.2018.07.006

    Article  PubMed  Google Scholar 

  36. Yap MH, Pons G, Martí J, Ganau S, Sentís M, Zwiggelaar R, Davison AK, Martí R (2018) Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J Biomed Heal Informatics 22:1218–1226. https://doi.org/10.1109/JBHI.2017.2731873

    Article  Google Scholar 

  37. Hu P, Wu F, Peng J, Liang P, Kong D (2016) Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution. Phys Med Biol 61:8676–8698. https://doi.org/10.1088/1361-6560/61/24/8676

    Article  PubMed  Google Scholar 

  38. Reddy DS, Bharath R, Rajalakshmi P (2018) A novel computer-aided diagnosis framework using deep learning for classification of fatty liver disease in ultrasound imaging. 2018 IEEE 20th Int Conf e-Health Networking. Appl Serv Heal 2018:1–5. https://doi.org/10.1109/HealthCom.2018.8531118

    Article  Google Scholar 

  39. Song W, Li S, Liu J, Qin H, Zhang B, Shuyang Z, Hao A (2015) Multi-task cascade convolution neural networks for automatic thyroid nodule detection and recognition. IEEE J Biomed Heal Informatics 14:1–11. https://doi.org/10.1109/JBHI.2018.2852718

    Article  CAS  Google Scholar 

  40. Ravishankar H, Prabhu S, Vaidya V, Singhal N (2016) Hybrid approach for automatic segmentation of fetal abdomen from ultrasound images using deep learning. IEEE 13th Int Symp Biomed Imaging 779–782. https://doi.org/10.1109/ISBI.2016.7493382

  41. Jinlian M, Dexing K (2018) Deep learning models for segmentation of lesion based on ultrasound images. Adv Ultrasound Diagnosis Ther 2:82. https://doi.org/10.37015/audt.2018.180804

    Article  Google Scholar 

  42. Li X, Wang S, Wei X, Zhu J, Yu R, Zhao M, Yu M, Liu Z, Liu S (2018) Fully convolutional networks for ultrasound image segmentation of thyroid nodules. 2018 IEEE 20th Int Conf High Perform Comput Commun IEEE 16th Int Conf Smart City; IEEE 4th Int Conf Data Sci Syst 886–890. https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00147

  43. Wang J, Li S, Song W, Qin H, Zhang B (2018) Learning from weakly-labeled clinical data for automatic thyroid nodule classification in ultrasound images. IEEE Int Conf Image Process 3114–3118. https://doi.org/10.1109/ICIP.2018.8451085

  44. Zhou S, Wu H, Gong J, Le T, Wu H, Chen Q, Xu Z (2018) Mark-guided segmentation of ultrasonic thyroid nodules using deep learning. Proc 2nd Int Symp Image Comput Digit Med 21–26. https://doi.org/10.1145/3285996.3286001

  45. Ying X, Yu Z, B RY, Li X, Yu M (2018) Thyroid nodule segmentation in ultrasound images based on cascaded convolutional neural network. IntConf Neural Inf Process 2:373–384.https://doi.org/10.1007/978-3-030-04224-0

  46. Poudel P, Illanes A (2019) Performance evaluation of U-Net convolutional neural network on different percentages of training data for thyroid ultrasound image segmentation. 41st Annu Int Conf IEEE Eng Med Biol Soc 2–5

  47. Ding J, Huang Z, Shi M, Ning C (2019) Automatic thyroid ultrasound image segmentation based on U-shaped network. 12th Int Congr Image Signal Process Biomed Eng Informatics, CISP-BMEI 1–5. https://doi.org/10.1109/CISP-BMEI48845.2019.8966062

  48. Kumar V, Webb J, Gregory A, Meixner DD, Knudsen JM, Callstrom M, Fatemi M, Alizad A (2020) Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. IEEE Access 8:63482–63496. https://doi.org/10.1109/ACCESS.2020.2982390

    Article  PubMed  PubMed Central  Google Scholar 

  49. Webb JM, Meixner DD, Adusei SA, Polley EC, Fatemi M, Alizad A (2021) Automatic deep learning semantic segmentation of ultrasound thyroid cineclips using recurrent fully convolutional networks. IEEE Access 9:5119–5127. https://doi.org/10.1109/ACCESS.2020.3045906

    Article  PubMed  Google Scholar 

  50. Shenoy NR, Jatti DA (2020) Segmentation of thyroid nodules using Improvised U-Net Architecture. Int J Innov Technol Explor Eng 9:56–60. https://doi.org/10.35940/ijitee.h6142.069820

    Article  Google Scholar 

  51. Wu Y, Shen X, Bu F, Tian J (2020) Ultrasound image segmentation method for thyroid nodules using ASPP fusion features. IEEE Access 8:172457–172466. https://doi.org/10.1109/access.2020.3022249

    Article  Google Scholar 

  52. Pan H, Zhou Q, Latecki LJ (2021) SGUNET: Semantic guided UNET for thyroid nodule segmentation. IEEE 18th Int Symp Biomed Imaging 630–634. https://doi.org/10.1109/ISBI48211.2021.9434051

  53. Shahroudnejad A, Vega R, Forouzandeh A, Balachandran S, Jaremko J, Noga M, Hareendranathan AR, Kapur J, Punithakumar K (2021) Thyroid nodule segmentation and classification using deep convolutional neural network and rule-based classifiers. Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS 3118–3121. https://doi.org/10.1109/EMBC46164.2021.9629557

  54. Gong H, Chen G, Wang R, Xie X, Mao M, Yu Y, Chen F, Li G (2021) Multi-task learning for thyroid nodule segmentation with thyroid region prior. IEEE 18th Int Symp Biomed Imaging (ISBI 257–261

  55. Gomes Ataide EJ, Agrawal S, Jauhari A, Boese A, Illanes A, Schenke S, Kreissl MC, Friebe M (2021) Comparison of deep learning algorithms for semantic segmentation of ultrasound thyroid nodules. Curr Dir Biomed Eng 7:879–882. https://doi.org/10.1515/cdbme-2021-2224

    Article  Google Scholar 

  56. Kang Q, Lao Q, Li Y, Jiang Z (2022) Thyroid nodule segmentation and classification in ultrasound images through intra- and inter-task consistent learning. Med Image Anal 79. https://doi.org/10.1016/j.media.2022.102443

  57. Tao Z, Dang H, Shi Y, Wang W, Wang X, Ren S (2022) Local and context-attention adaptive LCA-Net for thyroid nodule segmentation in ultrasound images. Sensors 22:1–19. https://doi.org/10.3390/s22165984

    Article  CAS  Google Scholar 

  58. Gan J, Zhang R (2022) Ultrasound image segmentation algorithm of thyroid nodules based on improved U-Net network. 3rd International Conference on Control, Robotics and Intelligent System (CCRIS 22) 61–66. https://doi.org/10.1145/3562007.3562018

  59. Nguyen DT, Choi J, Park KR (2022) Thyroid nodule segmentation in ultrasound image based on information fusion of suggestion and enhancement networks. Mathematics 10. https://doi.org/10.3390/math10193484

  60. Nie X, Zhou X, Tong T, Lin X, Wang L, Zheng H, Li J, Xue E, Chen S, Zheng M, Chen C, Du M (2022) N-Net: a novel dense fully convolutional neural network for thyroid nodule segmentation. Front Neurosci 16. https://doi.org/10.3389/fnins.2022.872601

  61. Shan T, Yan J, Cui X, Xie L (2022) DSCA-Net: a depthwise separable convolutional neural network with attention mechanism for medical image segmentation. Math Biosci Eng 20:365–382. https://doi.org/10.3934/mbe.2023017

    Article  PubMed  Google Scholar 

  62. Jianning Chi ZL (2023) Hybrid transformer UNet for thyroid segmentation from ultrasound scans. Comput Biol Med 153:106453. https://doi.org/10.1016/j.compbiomed.2022.106453

    Article  PubMed  Google Scholar 

  63. Haifan Gong JC (2023) Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules. Comput Biol Med 155:106389. https://doi.org/10.1016/j.compbiomed.2022.106389

    Article  PubMed  Google Scholar 

  64. Dedong Yang YL (2023) Multi-task thyroid tumor segmentation based on the joint loss function. Biomed Signal Process Control 79(2):104249. https://doi.org/10.1016/j.bspc.2022.104249

    Article  Google Scholar 

  65. Poudel P, Illanes A, Sheet D, Friebe M (2018) Evaluation of commonly used algorithms for thyroid ultrasound images segmentation and improvement using machine learning approaches. Hindawi J Healthc Eng 2018:1–13. https://doi.org/10.1155/2018/8087624

    Article  Google Scholar 

  66. Sun J, Sun T, Yuan Y, Zhang X, Shi Y, Lin Y (2018) Automatic diagnosis of thyroid ultrasound image based on FCN-AlexNet and transfer learning. IEEE 23rd Int Conf Digit Signal Process 1–5. https://doi.org/10.1109/ICDSP.2018.8631796

  67. M B, Wildman-Tobriner B CK (2019) Deep learning-based segmentation of nodules in thyroid ultrasound: improving performance by utilizing markers present in the images. Ultrasound Med Biol 415–421. https://doi.org/10.1016/j.ultrasmedbio.2019.10.003

  68. Guo Z, Zhou J, Zhao D (2020) Thyroid nodule ultrasonic imaging segmentation based on a deep learning model and data augmentation. IEEE 4th Inf Technol Autom Control Conf (ITNEC 2020) 549–554. https://doi.org/10.1109/ITNEC48623.2020.9085093

  69. Wu J, Zhang Z, Zhao J, Qiang Y (2020) Ultrasound image segmentation of thyroid nodules based on joint up-sampling. J Phys Conf Ser 1651. https://doi.org/10.1088/1742-6596/1651/1/012157

  70. Yadav N, Dass R, Virmani J (2022) Objective assessment of segmentation models for thyroid ultrasound images. J Ultrasound. https://doi.org/10.1007/s40477-022-00726-8

    Article  PubMed  Google Scholar 

  71. Zhou X, Chen Y, Liu S (2022) Ultrasound image segmentation of thyroid nodules based on U_Net. EasyChair Prepr 8983:1–10

    Google Scholar 

  72. Ke W, Wang Y, Wan P, Liu W (2017) An ultrasonic image recognition method for papillary thyroid carcinoma based on depth convolution neural network. Neural Inf Process ICONIP 2017 Lect Notes Comput Sci 10635:82–91. https://doi.org/10.1007/978-3-319-70096-0_9

    Article  Google Scholar 

  73. Wang Y, Ke W, Wan P (2018) A method of ultrasonic image recognition for thyroid papillary carcinoma based on deep convolution neural network. NeuroQuantology 16:757–768. https://doi.org/10.14704/nq.2018.16.5.1306

    Article  Google Scholar 

  74. Li H, Weng J, Shi Y, Gu W, Mao Y, Wang Y, Liu W, Zhang J (2018) An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images. Sci Rep 8:1–12. https://doi.org/10.1038/s41598-018-25005-7

    Article  CAS  Google Scholar 

  75. Liu T, Guo Q, Lian C, Ren X, Liang S, Yu J, Niu L, Sun W, Shen D (2019) Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks. Med Image Anal 58:101555. https://doi.org/10.1016/j.media.2019.101555

    Article  PubMed  Google Scholar 

  76. Wang L, Yang S, Yang S, Zhao C, Tian G, Gao Y, Chen Y, Lu Y (2019) Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network. World J Surg Oncol 17:1–9. https://doi.org/10.1186/s12957-019-1558-z

    Article  Google Scholar 

  77. Xie S, Yu J, Liu T, Chang Q, Niu L, Sun W (2019) Thyroid nodule detection in ultrasound images with convolutional neural networks. Proc 14th IEEE Conf Ind Electron Appl ICIEA 2019 1442–1446. https://doi.org/10.1109/ICIEA.2019.8834375

  78. Yu X, Wang H, Ma L (2020) Detection of thyroid nodules with ultrasound images based on deep learning. Curr Med Imaging 16:174–180. https://doi.org/10.2174/1573405615666191023104751

    Article  Google Scholar 

  79. Abdolali F, Kapur J, Jaremko JL, Noga M, Hareendranathan AR, Punithakumar K (2020) Automated thyroid nodule detection from ultrasound imaging using deep convolutional neural networks. Comput Biol Med 122. https://doi.org/10.1016/j.compbiomed.2020.103871

  80. Wang L, Zhang L, Zhu M, Qi X, Yi Z (2020) Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks. Med Image Anal 61:101665. https://doi.org/10.1016/j.media.2020.101665

    Article  PubMed  Google Scholar 

  81. Yao S, Yan J, Wu M, Yang X, Zhang W, Lu H, Qian B (2020) Texture synthesis based thyroid nodule detection from medical ultrasound images: interpreting and suppressing the adversarial effect of in-place manual annotation. Front Bioeng Biotechnol 8:1–11. https://doi.org/10.3389/fbioe.2020.00599

    Article  Google Scholar 

  82. Bo M, Mengxiang L, Xia L (2022) Ultrasound image segmentation method of thyroid nodules based on the improved U-Net network. J Electron Inf Technol 44:514–522. https://doi.org/10.11999/JEIT210015

    Article  Google Scholar 

  83. Dai H, Wufei X, Xia E, Yin P (2022) Ultrasonic thyroid automatic nodule segmentation method based on Sevnet network. SSRN Electron J. https://doi.org/10.2139/ssrn.4070526

    Article  Google Scholar 

  84. Chi J, Walia E, Babyn P, Wang J, Groot G, Eramian M (2017) Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. J Digit Imaging 30:477–486. https://doi.org/10.1007/s10278-017-9997-y

    Article  PubMed  PubMed Central  Google Scholar 

  85. Zhu Y, Fu Z, Fei J (2017) An image augmentation method using convolutional network for thyroid nodule classification by transfer learning. 2017 3rd IEEE Int Conf Comput Commun ICCC 2017 2018-Janua:1819–1823. https://doi.org/10.1109/CompComm.2017.8322853

  86. Sundar KVS, Rajamani KT, Sai SSS Exploring image classification of thyroid ultrasound images using deep learning. In: Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB), Lecture Notes in Computational Vision and Biomechanics 30. Springer International Publishing, pp 1635–1641

  87. Li X, Zhang S, Zhang Q, Wei X, Pan Y, Zhao J, Xin X, Qin C, Wang X, Li J, Yang F (2018) Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images : a retrospective, multicohort, diagnostic study. Lancet Oncol 2045:1–9. https://doi.org/10.1016/S1470-2045(18)30762-9

    Article  Google Scholar 

  88. Chang C, Huang H, Chen S (2010) Automatic thyroid nodule segmentation and component analysis in ultrasound images. Biomed Eng Appl Basis Commun 22:81–89. https://doi.org/10.4015/S1016237210001803

    Article  Google Scholar 

  89. Snekhalatha U, Gomathy V (2018) Ultrasound thyroid image segmentation, feature extraction, and classification of disease using feed forward back propagation network. Prog Adv Comput Intell Eng 563:89–98. https://doi.org/10.1007/978-981-10-6872-0_9

    Article  Google Scholar 

  90. Koundal D, Gupta S, Singh S (2018) Computer aided thyroid nodule detection system using medical ultrasound images. Biomed Signal Process Control 40:117–130. https://doi.org/10.1016/j.bspc.2017.08.025

    Article  Google Scholar 

  91. Gireesha HM, Nanda S (2014) Thyroid nodule segmentation and classification in ultrasound images. Int J Eng Res Technol 3:2252–2256

    Google Scholar 

  92. Gireesha H, S N (2014) Thyroid nodule segmentation and classification in ultrasound images. Int J Eng Res Technol 3:2252–56

  93. Singh N, Jindal A (2012) A segmentation method and comparison of classification methods for thyroid ultrasound images. Int J Comput Appl 50:43–49. https://doi.org/10.5120/7818-1115

    Article  Google Scholar 

  94. Abbasian Ardakani A, Bitarafan-Rajabi A, Mohammadzadeh A, Mohammadi A, Riazi R, Abolghasemi J, Homayoun Jafari A, Bagher Shiran M (2019) A hybrid multilayer filtering approach for thyroid nodule segmentation on ultrasound images. J Ultrasound Med 38:629–640. https://doi.org/10.1002/jum.14731

    Article  PubMed  Google Scholar 

  95. Nugroho HA, Nugroho A, Choridah L (2015) Thyroid nodule segmentation using active contour bilateral filtering on ultrasound images. Int Conf Qual Res 2015:43–46. https://doi.org/10.1109/QiR.2015.7374892

    Article  Google Scholar 

  96. Selvathi D, Sharnitha VVSS (2011) Thyroid classification and segmentation in ultrasound images using machine learning algorithms. International Conference on Signal Processing, Communication, Computing and Networking Technologies (ICSCCN-2011) 836–841. https://doi.org/10.1109/ICSCCN.2011.6024666

  97. Liu X, Deng Z, Yang Y (2018) Recent progress in semantic image segmentation. Artif Intell Rev 1–18. https://doi.org/10.1007/s10462-018-9641-3

  98. Ma J, Wu F, Jiang T, Zhao Q, Kong D (2017) Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks. Int J Comput Assist Radiol Surg 12:1895–1910. https://doi.org/10.1007/s11548-017-1649-7

    Article  PubMed  Google Scholar 

  99. Rezaee M, Zhang Y, Mishra R, Tong F, Tong H (2018) Using a VGG-16 Network for individual tree species detection with an object-based approach. 2018 10th IAPR Work Pattern Recognit Remote Sens 1–7. https://doi.org/10.1109/PRRS.2018.8486395

  100. Lian S, Luo Z, Zhong Z, Lin X, Su S, Li S (2018) Attention guided U-Net for accurate iris segmentation. J Vis Commun Image Represent 56:296–304. https://doi.org/10.1016/j.jvcir.2018.10.001

    Article  Google Scholar 

  101. Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. MICCAI 2015(arXiv:150504597) 1–8

  102. Yao W, Zeng Z, Lian C, Tang H (2018) Pixel-wise regression using U-Net and its application on pansharpening. Neurocomputing. https://doi.org/10.1016/j.neucom.2018.05.103

    Article  Google Scholar 

  103. Xue Y, Xu T, Zhang H, Long R, Huang X (2017) SegAN: adversarial network with multi-scale L1 loss for medical image segmentation. Neuroinformatics c:1–9. https://doi.org/10.1016/B978-012264841-0/50037-8

  104. China D, Illanes A, Poudel P, Friebe M, Mitra P, Sheet D (2019) Anatomical structure segmentation in ultrasound volumes using cross frame belief propagating iterative random walks. IEEE J Biomed Heal Inf 23:1110–1118. https://doi.org/10.1109/JBHI.2018.2864896

    Article  Google Scholar 

  105. Lestari DP, Madenda S, Ernastuti E, Wibowo EP (2017) Comparison of three segmentation methods for breast ultrasound images based on level set and morphological operations. Int J Electr Comput Eng 7:383–391. https://doi.org/10.11591/ijece.v7i1.pp383-391

    Article  Google Scholar 

  106. Prabha DS, Kumar JS (2016) Performance evaluation of image segmentation using objective methods. Indian J Sci Technol 9:1–8. https://doi.org/10.17485/ijst/2016/v9i8/87907

    Article  Google Scholar 

  107. Trivedi HM, Panahiazar M, Liang A, Lituiev D, Chang P, Sohn JH, Chen YY, Franc BL, Joe B, Hadley D (2018) Large scale semi-automated labeling of routine free-text clinical records for deep learning. J Digit Imaging. https://doi.org/10.1007/s10278-018-0105-8

    Article  PubMed  PubMed Central  Google Scholar 

  108. De Brébisson A, Montana G (2015) Deep neural networks for anatomical brain segmentation. IEEE Comput Soc Conf Comput Vis Pattern Recognit Work 2015-Octob:20–28. https://doi.org/10.1109/CVPRW.2015.7301312

  109. Lu F, Wu F, Hu P, Peng Z, Kong D (2017) Automatic 3D liver location and segmentation via convolutional neural network and graph cut. Int J Comput Assist Radiol Surg 12:171–182. https://doi.org/10.1007/s11548-016-1467-3

    Article  PubMed  Google Scholar 

  110. Da Nóbrega RVM, Peixoto SA, Da Silva SPP, Filho PPR (2018) Lung nodule classification via deep transfer learning in CT lung images. Proc - IEEE Symp Comput Med Syst 2018-June:244–249. https://doi.org/10.1109/CBMS.2018.00050

  111. Hermessi H, Mourali O, Zagrouba E (2019) Deep feature learning for soft tissue sarcoma classification in MR images via transfer learning. Expert Syst Appl 120:116–127. https://doi.org/10.1016/j.eswa.2018.11.025

    Article  Google Scholar 

  112. Rezatofighi H, Tsoi N, Gwak J, Sadeghian A, Reid I, Savarese S (2019) Generalized intersection over union: a metric and a loss for bounding box regression. CVPR 2019:1–9

    Google Scholar 

  113. Rahman MA, Wang Y (2016) Optimizing intersection-over-union in deep neural networks for image segmentation. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 10072 LNCS:234–244. https://doi.org/10.1007/978-3-319-50835-1_22

Download references

Acknowledgements

The authors would like to thank Dr. Jyotsna Sen, Sr. Professor, department of radiodiagnosis, Pt. B. D. Sharma Postgraduate Institute of Medical Sciences, Rohtak, for stimulating discussions regarding different sonographic characteristics exhibited by various types of benign and malignant thyroid tumors. The first author acknowledges “National Project Implementation Unit (NPIU), a unit of the Ministry of Human Resource Development, Government of India” for the financial assistantship through the TEQIP-III project at Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Haryana, India.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology Murthal, Sonepat, 131039, India

    Niranjan Yadav & Rajeshwar Dass

  2. Central Scientific Instruments Organization, Council of Scientific and Industrial Research, Chandigarh, 160030, India

    Jitendra Virmani

Authors
  1. Niranjan Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajeshwar Dass
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jitendra Virmani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Niranjan Yadav.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yadav, N., Dass, R. & Virmani, J. Assessment of encoder-decoder-based segmentation models for thyroid ultrasound images. Med Biol Eng Comput 61, 2159–2195 (2023). https://doi.org/10.1007/s11517-023-02849-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-023-02849-4

Keywords

Search

Navigation