Ultrasonic Image Segmentation of Thyroid Nodules Based on HA-UNet++

doi:10.3969/j.issn.1006-2475.2025.03.014

Abstract

Abstract: Thyroid disease is one of the most frequently diagnosed nodular lesions in adult population， and it’s incidence is increasing year by year. With the development of artificial intelligence technology， the automatic diagnosis of thyroid ultrasound images using computer vision technology can significantly improve the accuracy and efficiency of diagnosis. However， most image segmentation methods based on deep learning， limited by the size of receptive field， cannot focus on the important features of the image in time and extract them effectively， resulting in low segmentation accuracy. In order to solve the above problems， a new deep learning network model HA-UNet ++ （Hybrid Dilated Convolution-Attention-UNet++） is adopted in this paper to segment ultrasonic images of thyroid nodules. HA-UNet ++ improves backbone network structure at each stage of encoding path. At the same time， hybrid dilated convolution is added to the convolution blocks with three layers of convolution in the network， and attention mechanism is added to each convolution block， so that it can quickly predict the enhanced thyroid nodule data set. On this basis， thyroid nodules are labeled and segmented.

Key words: , image segmentation, thyroid nodules, hybrid dilated convolution, attention mechanism, U-Net++

CLC Number:

TP391

ZHU Yongtian, TIAN Fei, DONG Baoliang. Ultrasonic Image Segmentation of Thyroid Nodules Based on HA-UNet++[J]. Computer and Modernization, 2025, 0(03): 93-98.

References

［1］周晓松，赵涓涓. 多尺度监督U-Net甲状腺结节超声图像分割［J］. 太原理工大学学报， 2022，53（6）：1134-1142.
［2］ CUN Y L， BOSER B， DENKER J， et al. Handwritten digit recognition with a backpropagation network［M］// Advances in Neural Information Processing Systems. ACM， 1990：396-404.
［3］ SIMONYAN K， ZISSERMAN A. Very deep convolutional networks for large-scale image recognition［J］. Computer Science， 2014. DOI： 10.48550/arXiv.1409.1556.
［4］ KUMAR D， WONG A， CLAUSI D A. Lung nodule classification using deep features in CT images［C］// 2015 12th Conference on Computer and Robot Vision. IEEE， 2015：133-138.
［5］ KAMPHENKEL J， JAEGER P F， BICKELHAUPT S， et al. Domain adaptation for deviating acquisition protocols in CNN-based lesion classification on diffusion-weighted MR images［C］// International Workshop on Reconstruction and Analysis of Moving Body Organs. Spring， 2018：73-80.
［6］ LONG J， SHELHAMER E， DARRELL T. Fully convolutional networks for semantic segmentation［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2015，39（4）：640-651.
［7］ NIE D， WANG L， GAO Y Z， et al. Fully convolutional networks for multi-modality isointense infant brain image segmentation［C］// 2016 IEEE 13th International Symposium on Biomedical Imaging （ISBI）. IEEE， 2016：1342-1345.
［8］ VORONTSOV E， TANG A， PAL C， et al. Liver lesion segmentation informed by joint liver segmentation［C］// 2018 IEEE 15th International Symposium on Biomedical Imaging. 2018：1332-1335.
［9］ RONNEBERGER O， FISCHER P， BROX T. U-Net： Convolutional networks for biomedical image segmentation［C］// International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer， 2015：234-241.
［10］ ZHOU Z， SIDDIQUEE M M R， TAJBAKHSH N， et al. UNet++： Redesigning skip connections to exploit multiscale features in image segmentation［J］. IEEE Transactions on Medical Imaging， 2019，39（6）：1856-1867.
［11］ YU F， KOLTUN V. Multi-scale context aggregation by dilated convolutions［J］. arXiv preprint arXiv：1511.07122， 2015.
［12］ WANG P Q， CHEN P F， YUAN Y， et al. Understanding convolution for semantic segmentation［C］// 2018 IEEE Winter Conference on Applications of Computer Vision. IEEE， 2018：1451-1460.
［13］ BAHDANAU D， CHO K， BENGIO Y. Neural machine translation by jointly learning to align and translate［J］. arXiv preprint arXiv：1409.0473， 2014.
［14］ VASWANI A， SHAZEER N， PARMAR N， et al. Attention is all you need［C］// Proceedings of the 31st International Conference on Neural Information Processing Systems. ACM， 2017：6000-6010.
［15］ DOSOVITSKIY A， BEYER L， KOLESNIKOV A， et al. An image is worth 16×16 words： Transformers for image recognition at scale［J］. arXiv preprint arXiv：2010.11929， 2020.
［16］ WOO S， PARK J， LEE J Y， et al. CBAM： Convolutional block attention module［C］// Proceedings of the European Conference on Computer Vision. Spring， 2018：3-19.
［17］ KINGMA D P， BA J. ADAM： A method for stochastic optimization［J］. arXiv preprint arXiv：1412.6980， 2014.
［18］ GONG H F， CHEN G Q， WANG R R， et al. Multi-task learning for thyroid nodule segmentation with thyroid region prior［C］//2021 IEEE 18th International Symposium on Biomedical Imaging. IEEE， 2021：257-261.
［19］ GONG H F， CHEN J X， CHEN G Q， et al. Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules［J］. Computers in Biology and Medicine， 2023，155. DOI： 10.1016/j.compbiomed.2022.106389.
［20］ BUSLAEV A， IGLOVIKOV V I， KHVEDCHENYA E， et al. Albumentations： Fast and flexible image augmentations［J］. Information， 2020， 11（2）. DOI： 10.3390/info11020125.
［21］ BOUREAU Y L， PONCE J， LECUN Y. A theoretical analysis of feature pooling in visual recognition［C］// Proceedings of the 27th International Conference on Machine Learning. Springer， 2010：111-118.
［22］ LECUN Y， BOTTOU L， BENGIO Y， et al. Gradient-based learning applied to document recognition［J］. Proceedings of the IEEE， 1998，86（11）：2278-2324.
［23］ SUDRE C H， LI W， VERCAUTEREN T， et al. Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations［C］// International Workshop on Deep Learning in Medical Image Analysis. Springer，
2017：240-248.
［24］ PAN H T， ZHOU Q， LATECKI L J. Sgunet： Semantic guided unet for thyroid nodule segmentation［C］// 2021 IEEE 18th International Symposium on Biomedical Imaging. IEEE， 2021：630-634.
［25］ BADRINARAYANAN V， KENDALL A， CIPOLLA R. SegNet： A deep convolutional encoder-decoder architecture for image segmentation［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2017，39（12）：2481-2495.
［26］ FENG S L， ZHAO H M， SHI F， et al. CPFNet： Context pyramid fusion network for medical image segmentation［J］. IEEE Transactions on Medical Imaging， 2020，39（10）：3008-3018.
［27］ CHEN J N， LU Y Y， YU Q H， et al. TransUNet： Transformers make strong encoders for medical image segmentation［J］. arXiv preprint arXiv：2102.04306， 2021.

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