Ultrasound Image Segmentation of Thyroid Nodule via Latent Semantic Feature Co-Registration

• URL: http://arxiv.org/abs/2310.09221v2
• Date: Mon, 22 Jan 2024 04:48:57 GMT
• Title: Ultrasound Image Segmentation of Thyroid Nodule via Latent Semantic Feature Co-Registration
• Authors: Xuewei Li, Yaqiao Zhu, Jie Gao, Xi Wei, Ruixuan Zhang, Yuan Tian, and ZhiQiang Liu
• Abstract summary: The present paper proposes ASTN, a framework for thyroid nodule segmentation achieved through a new type co-registration network. By extracting latent semantic information from the atlas and target images, this framework can ensure the integrity of anatomical structure. This paper also provides an atlas selection algorithm to mitigate the difficulty of co-registration.
• Score: 12.211161441766532
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Segmentation of nodules in thyroid ultrasound imaging plays a crucial role in the detection and treatment of thyroid cancer. However, owing to the diversity of scanner vendors and imaging protocols in different hospitals, the automatic segmentation model, which has already demonstrated expert-level accuracy in the field of medical image segmentation, finds its accuracy reduced as the result of its weak generalization performance when being applied in clinically realistic environments. To address this issue, the present paper proposes ASTN, a framework for thyroid nodule segmentation achieved through a new type co-registration network. By extracting latent semantic information from the atlas and target images and utilizing in-depth features to accomplish the co-registration of nodules in thyroid ultrasound images, this framework can ensure the integrity of anatomical structure and reduce the impact on segmentation as the result of overall differences in image caused by different devices. In addition, this paper also provides an atlas selection algorithm to mitigate the difficulty of co-registration. As shown by the evaluation results collected from the datasets of different devices, thanks to the method we proposed, the model generalization has been greatly improved while maintaining a high level of segmentation accuracy.

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