Abstract
The thyroid nodule is quickly increasing worldwide and the thyroid ultrasound is the key tool for the diagnosis of it. For the subtle difference between malignant and benign nodules, segmenting lesions is the crucial preliminary step for diagnosis. In this paper, we propose a low-resolution-to-high-resolution segmentation framework for TN-SCUI2020 challenge to alleviate the workload of clinicians and improve the efficiency of diagnosis. Specifically speaking, in order to integrate multi-scale information, several low-resolution segmenting results are obtained firstly and combined with a high-resolution image to refine them and obtain high-resolution results. Secondly, iterative-transfer is proposed to effectively initialize network based on previous trained one on small-scale images. Finally, ensemble refinement is introduced to utilize multiple models to refine the segmentation again. Experimental results showed the effectiveness of the proposed framework. And we won the 2nd place in the segmentation task of TN-SCUI2020.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
http://www.thyroid.org/wp-content/uploads/patients/brochures/Nodules_brochure.pdf
https://www.cancer.net/cancer-types/thyroid-cancer/statistics
Chen, H., Wang, X., Huang, Y., Wu, X., Yu, Y., Wang, L.: Harnessing 2D networks and 3D features for automated pancreas segmentation from volumetric CT images. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 339–347. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_38
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition, pp. 770–778 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014)
Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks, pp. 6105–6114 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, H., Song, S., Wang, X., Wang, R., Meng, D., Wang, L. (2021). LRTHR-Net: A Low-Resolution-to-High-Resolution Framework to Iteratively Refine the Segmentation of Thyroid Nodule in Ultrasound Images. In: Shusharina, N., Heinrich, M.P., Huang, R. (eds) Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data. MICCAI 2020. Lecture Notes in Computer Science(), vol 12587. Springer, Cham. https://doi.org/10.1007/978-3-030-71827-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-71827-5_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71826-8
Online ISBN: 978-3-030-71827-5
eBook Packages: Computer ScienceComputer Science (R0)