Abstract
Due to the noises captured in ultrasound device and image reconstruction process, the edges of thyroid nodule are usually not distinctive and it is very difficult for existing approaches to well segment them in ultrasound images. While deep neural networks like U-Net have been successfully applied in many medical image segmentation tasks, their segmentation performances on ultrasound images are still not satisfactory. To address this issue, we propose in this paper a boundary field regression branch to provide useful boundary information to help improve the segmentation performance of existing networks. Without requirement of additional labeling costs, our approach firstly generates boundary field heatmap from available segmentation masks, which are then used as a supervision to train the regression branch. As a general architecture, our branch can be integrated with all encoder-decoder like segmentation networks. A dataset consisting of 3169 images from 2004 patients is used for experiments. We integrate our branch with U-Net, Attention U-Net, U-Net++ and DeepLabv3+; consistent improvements of Dice metrics were observed. The memory and computation costs required by adding our branch are marginal as well.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Cui Y, Mubarik S, Li R, Yu C et al (2021) Trend dynamics of thyroid cancer incidence among china and the us adult population from 1990 to 2017: a joinpoint and age-period-cohort analysis. BMC Public Health 21(1):1–13
Popoveniuc G, Jonklaas J (2012) Thyroid nodules. Medical Clinics 96(2):329–349
Bomeli SR, LeBeau SO, Ferris RL (2010) Evaluation of a thyroid nodule. Otolaryngologic Clinics of North America 43(2):229–238
Yokozawa T, Fukata S, Kuma K, Matsuzuka F, Kobayashi A, Hirai K, Miyauchi A, Sugawara M (1996) Thyroid cancer detected by ultrasound-guided fine-needle aspiration biopsy. World journal of surgery 20(7):848–853
Chen J, You H, Li K (2020) A review of thyroid gland segmentation and thyroid nodule segmentation methods for medical ultrasound images. Computer methods and programs in biomedicine 185:105329
Song W, Li S, Liu J, Qin H, Zhang B, Zhang S, Hao A (2019) Multitask cascade convolution neural networks for automatic thyroid nodule detection and recognition. IEEE Journal of Biomedical and Health Informatics 23:1215–1224
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. 3431–3440
Ronneberger O, Fischer P, Brox T (2015) "U-net: Convolutional networks for biomedical image segmentation." In International Conference on Medical image computing and computer-assisted intervention. Springer, New York. 234–241
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). 801–818
He X, Zemel RS, Carreira-Perpinán MA (2004) “Multiscale conditional random fields for image labeling,” in Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2004., 2. minus IEEE, 2004, pp. II–II
Kervadec H, Bouchtiba J, Desrosiers C, Granger E, Dolz J, Ayed IB (2019) “Boundary loss for highly unbalanced segmentation,” in International conference on medical imaging with deep learning. minus PMLR, pp. 285–296
Karimi D, Salcudean S (2020) Reducing the hausdorff distance in medical image segmentation with convolutional neural networks. IEEE Transactions on Medical Imaging 39:499–513
Shih FY, Pu CC (1995) A skeletonization algorithm by maxima tracking on euclidean distance transform. Pattern Recognition 28(3):331–341
Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018) “Unet++: A nested u-net architecture for medical image segmentation,” 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, S..., 11045, 3–11
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 preprintarXiv:1804.03999
Li X, Chen H, Qi X, Dou Q, Fu C, Heng P (2018) H-denseunet: Hybrid densely connected unet for liver and tumor segmentation from ct volumes. IEEE Transactions on Medical Imaging 37:2663–2674
Maroulis DE, Savelonas MA, Iakovidis DK, Karkanis SA, Dimitropoulos N (2007) Variable background active contour model for computer-aided delineation of nodules in thyroid ultrasound images. IEEE Transactions on Information Technology in Biomedicine 11(5):537–543
Ma J, Wu F, Jiang T, Zhao Q, Kong D (2017) Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks. International journal of computer assisted radiology and surgery 12(11):1895–1910
Tang Z, Ma J (2020) Coarse to fine ensemble network for thyroid nodule segmentation. Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data 12587:122–128
Pan H, Zhou Q, Latecki L (2021) “Sgunet: Semantic guided unet for thyroid nodule segmentation,” 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 630–634
Wang M, Yuan C, Wu D, Zeng Y, Zhong S, Qiu W (2021) Automatic Segmentation and Classification of Thyroid Nodules in Ultrasound Images with Convolutional Neural Networks. In: Shusharina N, Heinrich MP, Huang R (eds) Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data. Springer International Publishing, Cham, pp 109–115
Kumar V, Webb JM, Gregory A, Meixner DD, Knudsen J, 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(63):482–496
Xue Y, Tang H, Qiao Z, Gong G, Yin Y, Qian Z, Huang C, Fan W, Huang X (2020) Shape-aware organ segmentation by predicting signed distance maps. Proceedings of the AAAI Conference on Artificial Intelligence 34(07):565–572
Luo X, Chen J, Song T, Chen Y, Wang G, Zhang S (2020) Semi-supervised medical image segmentation through dual-task consistency. Proceedings of the AAAI Conference on Artificial Intelligence. 35:8801–8809
Kim Y, Kim S, Kim T,Kim C “Cnn-based semantic segmentation using level set loss,” in 2019 IEEE winter conference on applications of computer vision (WACV). IEEE, 2019, pp. 1752–1760
Lee HJ, Kim JU, Lee S, Kim HG, Ro YM (2020) “Structure boundary preserving segmentation for medical image with ambiguous boundary,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4816–4825
Zhou J, Jia X, Ni D, Noble A, Huang R, Tan T, Van MT (2020) Thyroid nodule segmentation and classification in ultrasound images. 3
Acknowledgements
The work is supported by the National Natural Science Foundation of China under Grant 91959108 and 61976145, and Shenzhen Municipal Science and Technology Innovation Council under Grants JCYJ20190813100801664.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
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 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.
About this article
Cite this article
Jin, Z., Li, X., Zhang, Y. et al. Boundary regression-based reep neural network for thyroid nodule segmentation in ultrasound images. Neural Comput & Applic 34, 22357–22366 (2022). https://doi.org/10.1007/s00521-022-07719-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-022-07719-y