Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Tnseg: adversarial networks with multi-scale joint loss for thyroid nodule segmentation

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The thyroid gland is a critical regulator of numerous physiological functions, and the presence of thyroid nodules often signals potential disorders. Accurate nodule segmentation from ultrasound images is imperative for effective diagnosis and treatment planning. Existing techniques often struggle due to intra-nodule variability. To address this, we introduce TNSeg, an innovative framework specifically designed for thyroid nodule segmentation. TNSeg incorporates two key components: a segmentation block and a discriminative block, and leverages adversarial training. In particular, the discriminator uses a fully convolutional decoder with skip connections to efficiently differentiate between real and synthetic samples. Further, we introduce a novel multi-scale joint loss function for adversarial training that employs a balanced sampling strategy, effectively resolving the difficulties associated with foreground-background differentiation and computational redundancy. Extensive evaluation proves TNSeg’s superiority in achieving a Dice coefficient of 92.06%, Hd95 of 13.35, Jaccard index of 90.02%, and Precision of 94.01%, thereby demonstrating significant improvements in four commonly used segmentation quality metrics.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

References

  1. Paschou SA, Vryonidou A, Goulis DG (2017) Thyroid nodules: a guide to assessment, treatment and follow-up. Maturitas 96:1–9

    Article  PubMed  Google Scholar 

  2. Kim J, Gosnell JE, Roman SA (2020) Geographic influences in the global rise of thyroid cancer. Nat Rev Endocrinol 16(1):17–29

    Article  PubMed  Google Scholar 

  3. Alexander EK, Cibas ES (2022) Diagnosis of thyroid nodules. Lancet Diabetes Endocrinol 10(7):533–539

    Article  PubMed  Google Scholar 

  4. Fresilli D, David E, Pacini P, Del Gaudio G, Dolcetti V, Lucarelli GT, Di Leo N, Bellini MI, D’Andrea V, Sorrenti S et al (2021) Thyroid nodule characterization: how to assess the malignancy risk. Update of the literature. Diagnostics 11(8):1374

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Tessler FN, Middleton WD, Grant EG, Hoang JK, Berland LL, Teefey SA, Cronan JJ, Beland MD, Desser TS, Frates MC et al (2017) ACR thyroid imaging, reporting and data system (TI-RADS): white paper of the ACR TI-RADS committee. J Am Coll Radiol 14(5):587–595

    Article  PubMed  Google Scholar 

  6. Tuysuzoglu A, Tan J, Eissa K, Kiraly AP, Diallo M, Kamen A (2018) Deep adversarial context-aware landmark detection for ultrasound imaging. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, Sept 16–20, 2018, Proceedings, Part IV 11. Springer, pp 151–158

  7. Prete A, de Souza PB, Censi S, Muzza M, Nucci N, Sponziello M (2020) Update on fundamental mechanisms of thyroid cancer. Front Endocrinol 11:102

    Article  Google Scholar 

  8. Luo G, Zhang Y, Etxeberria J, Arnold M, Cai X, Hao Y, Zou H et al (2023) Projections of lung cancer incidence by 2035 in 40 countries worldwide: population-based study. JMIR Public Health Surveill 9(1):e43651

    Article  PubMed  PubMed Central  Google Scholar 

  9. Sorrenti S, Dolcetti V, Radzina M, Bellini MI, Frezza F, Munir K, Grani G, Durante C, D’Andrea V, David E et al (2022) Artificial intelligence for thyroid nodule characterization: where are we standing? Cancers 14(14):3357

    Article  PubMed  PubMed Central  Google Scholar 

  10. Tahmasebi A, Wang S, Daniels K, Cottrill E, Liu J-B, Xu J, Lyshchik A, Eisenbrey JR (2020) Ultrasonographic risk stratification of indeterminate thyroid nodules; a comparison of an artificial intelligence algorithm with radiologist performance. In: 2020 IEEE International Ultrasonics Symposium (IUS). IEEE, pp. 1–4

  11. Yin X-X, Sun L, Fu Y, Lu R, Zhang Y (2022) U-net-based medical image segmentation. J Healthc Eng. https://doi.org/10.1155/2022/4189781

    Article  PubMed  PubMed Central  Google Scholar 

  12. Shahroudnejad A, Qin X, Balachandran S, Dehghan M, Zonoobi D, Jaremko J, Kapur J, Jagersand M, Noga M, Punithakumar K (2021) Tun-det: a novel network for thyroid ultrasound nodule detection. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, Sept 27–Oct 1, 2021, Proceedings, Part I 24. Springer, pp 656–667

  13. Li X, Jiang Y, Li M, Yin S (2020) Lightweight attention convolutional neural network for retinal vessel image segmentation. IEEE Trans Ind Inform 17(3):1958–1967

    Article  Google Scholar 

  14. Li J, Chen J, Sheng B, Li P, Yang P, Feng DD, Qi J (2021) Automatic detection and classification system of domestic waste via multimodel cascaded convolutional neural network. IEEE Trans Ind Inform 18(1):163–173

    Article  Google Scholar 

  15. Zhou H, Zhang J, Lei J, Li S, Tu D (2016) Image semantic segmentation based on FCN-CRF model. In: 2016 International Conference on Image, Vision and Computing (ICIVC), pp 9–14

  16. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2881–2890

  17. Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848

    Article  PubMed  Google Scholar 

  18. Chen L-C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587

  19. Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 801–818

  20. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1251–1258

  21. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015: 18th International Conference, Munich, Germany, Oct 5-9, 2015, Proceedings, Part III 18. Springer, pp 234–241

  22. Xiao T, Liu Y, Zhou B, Jiang Y, Sun J (2018) Unified perceptual parsing for scene understanding. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 418–434

  23. Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B et al (2018) Attention u-net: learning where to look for the pancreas. arXiv:1804.03999

  24. Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018) Unet++: a nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, Sept 20, 2018, Proceedings 4. Springer, pp 3–11

  25. Yi X, Walia E, Babyn P (2019) Generative adversarial network in medical imaging: a review. Med Image Anal 58:101552

    Article  PubMed  Google Scholar 

  26. Wang D, Gu C, Wu K, Guan X (2017) Adversarial neural networks for basal membrane segmentation of microinvasive cervix carcinoma in histopathology images. In: 2017 International Conference on Machine Learning and Cybernetics (ICMLC), vol 2. IEEE, pp 385–389

  27. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in Neural Information Processing Systems, vol 30

  28. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16 × 16 words: transformers for image recognition at scale. arXiv:2010.11929

  29. Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y (2021) Transunet: transformers make strong encoders for medical image segmentation. arXiv:2102.04306

  30. Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M (2021) Swin-unet: Unet-like pure transformer for medical image segmentation. arXiv:2105.05537

  31. Ardakani AA, Bitarafan-Rajabi A, Mohammadzadeh A, Mohammadi A, Riazi R, Abolghasemi J, Jafari AH, Shiran MB (2019) A hybrid multilayer filtering approach for thyroid nodule segmentation on ultrasound images. J Ultrasound Med 38(3):629–640

    Article  Google Scholar 

  32. Ma J, Fa W, 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

    Article  PubMed  Google Scholar 

  33. Ying X, Yu Z, Yu R, Li X, Yu M, Zhao M, Liu K (2018) Thyroid nodule segmentation in ultrasound images based on cascaded convolutional neural network. In: Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, Dec 13–16, 2018, Proceedings, Part VI 25. Springer, pp 373–384

  34. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  35. 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

    Article  PubMed  PubMed Central  Google Scholar 

  36. Pan H, Zhou Q, Latecki LJ (2021) Sgunet: semantic guided unet for thyroid nodule segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, pp 630–634

  37. Song R, Zhu C, Zhang L, Zhang T, Luo Y, Liu J, Yang J (2022) Dual-branch network via pseudo-label training for thyroid nodule detection in ultrasound image. Appl Intell 52(10):11738–11754

    Article  Google Scholar 

  38. Sun J, Li C, Zhengda L, He M, Zhao T, Li X, Gao L, Xie K, Lin T, Sui J et al (2022) Tnsnet: thyroid nodule segmentation in ultrasound imaging using soft shape supervision. Comput Methods Programs Biomed 215:106600

    Article  PubMed  Google Scholar 

  39. Chen F, Ye H, Zhang D, Liao H (2022) Typeseg: a type-aware encoder-decoder network for multi-type ultrasound images co-segmentation. Comput Methods Programs Biomed 214:106580

    Article  PubMed  Google Scholar 

  40. Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA (2018) Generative adversarial networks: an overview. IEEE Signal Process Mag 35(1):53–65

    Article  ADS  Google Scholar 

  41. Pedraza L, Vargas C, Narváez F, Durán O, Muñoz E, Romero E (2015) An open access thyroid ultrasound image database. In: 10th International Symposium on Medical Information Processing and Analysis, vol 9287. SPIE, pp 188–193

  42. Gong H, Chen J, Chen G, Li H, Li G, Chen F (2023) Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules. Comput Biol Med 155:106389

    Article  PubMed  Google Scholar 

  43. Zhao R, Qian B, Zhang X, Li Y, Wei R, Liu Y, Pan Y (2020) Rethinking dice loss for medical image segmentation. In: 2020 IEEE International Conference on Data Mining (ICDM). IEEE, pp 851–860

  44. Xue Y, Xu T, Zhang H, Long LR, Huang X (2018) Segan: adversarial network with multi-scale l 1 loss for medical image segmentation. Neuroinformatics 16:383–392

    Article  PubMed  Google Scholar 

  45. Li L, Ma H (2022) Rdctrans u-net: a hybrid variable architecture for liver ct image segmentation. Sensors 22(7):2452

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  46. Yao C, Wang M, Zhu W, Huang H, Shi F, Chen Z, Wang L, Wang T, Zhou Y, Peng Y et al (2021) Joint segmentation of multi-class hyper-reflective foci in retinal optical coherence tomography images. IEEE Trans Biomed Eng 69(4):1349–1358

    Article  Google Scholar 

  47. 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. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, pp 257–261

  48. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3431–3440

  49. Feng S, Zhao H, Shi F, Cheng X, Wang M, Ma Y, Xiang D, Zhu W, Chen X (2020) Cpfnet: context pyramid fusion network for medical image segmentation. IEEE Trans Med Imaging 39(10):3008–3018

    Article  PubMed  Google Scholar 

  50. Chandra TB, Verma K, Singh BK, Jain D, Netam SS (2021) Coronavirus disease (Covid-19) detection in chest x-ray images using majority voting based classifier ensemble. Expert Syst Appl 165:113909

    Article  PubMed  Google Scholar 

  51. Chandra TB, Singh BK, Jain D (2022) Integrating patient symptoms, clinical readings, and radiologist feedback with computer-aided diagnosis system for detection of infectious pulmonary disease: a feasibility study. Med Biol Eng Comput 60(9):2549–2565

    Article  PubMed  Google Scholar 

  52. Chandra TB, Singh BK, Jain D (2022) Disease localization and severity assessment in chest x-ray images using multi-stage superpixels classification. Comput Methods Programs Biomed 222:106947

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This research was funded by the National Key Research and Development Program of China (Grant No. 2020YFB2103604).

Author information

Authors and Affiliations

  1. School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Yongyuan Road, Beijing, 100044, China

    Xiaoxuan Ma, Boyang Sun, Weifeng Liu, Dong Sui & Sihan Shan

  2. Department of Laboratory Diagnostics, Changhai Hospital Second Military Medical University, Changhai Road, Shanghai, 200433, China

    Jing Chen & Zhaofeng Tian

Authors
  1. Xiaoxuan Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Boyang Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weifeng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dong Sui
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sihan Shan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhaofeng Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, XM, BS and DS; methodology, BS and DS; software, BS, SS; validation, BS, XM and DS; formal analysis, DS, BS and SS; investigation, XM and SS; resources, DS; data curation, DS; writing—original draft preparation, BS and WL; writing—review and editing, XM, BS, WL, DS, ZT and JC; visualization, BS; supervision, XM; project administration, XM and DS; funding acquisition, XM. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Xiaoxuan Ma.

Ethics declarations

Ethics approval

Not applicable.

Conflict of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

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

Ma, X., Sun, B., Liu, W. et al. Tnseg: adversarial networks with multi-scale joint loss for thyroid nodule segmentation. J Supercomput 80, 6093–6118 (2024). https://doi.org/10.1007/s11227-023-05689-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-023-05689-z

Keywords

Search

Navigation