Hongcheng Han, Zhiqiang Tian, Qinbo Guo, Jue Jiang, Shaoyi Du, Juan Wang
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引用次数: 0
摘要
弹性超声成像在甲状腺癌和其他疾病的诊断中日益重要,但其对专业设备和技术的依赖限制了其广泛应用。本文提出了一种新型多模态超声诊断管道,通过将 B 超(BUS)图像转化为弹性成像图像(EUS),扩大了弹性成像超声的应用范围。此外,针对现有图像到图像转换方法难以有效模拟样本间变化和准确捕捉区域尺度结构一致性的局限性,我们提出了一种基于分层结构一致性的 BUS 到 EUS 转换方法。通过结合领域级、样本级、斑块级和像素级约束,我们的方法可引导模型学习从 BUS 到 EUS 的更精确映射,从而提高诊断准确性。实验结果表明,与在 STUSI 和 BUSI 数据集上仅使用 BUS 图像相比,在 MTUSI 数据集上,所提出的方法显著提高了 BUS 到 EUS 转换的准确性,而且生成的弹性成像图像提高了结节诊断的准确性。这一进展凸显了弹性成像技术在临床实践中更广泛应用的潜力。代码见 https://github.com/HongchengHan/HSC-T。
HSC-T: B-ultrasound-to-elastography Translation via Hierarchical Structural Consistency Learning for Thyroid Cancer Diagnosis.
Elastography ultrasound imaging is increasingly important in the diagnosis of thyroid cancer and other diseases, but its reliance on specialized equipment and techniques limits widespread adoption. This paper proposes a novel multimodal ultrasound diagnostic pipeline that expands the application of elastography ultrasound by translating B-ultrasound (BUS) images into elastography images (EUS). Additionally, to address the limitations of existing image-to-image translation methods, which struggle to effectively model inter-sample variations and accurately capture regional-scale structural consistency, we propose a BUS-to-EUS translation method based on hierarchical structural consistency. By incorporating domain-level, sample-level, patch-level, and pixel-level constraints, our approach guides the model in learning a more precise mapping from BUS to EUS, thereby enhancing diagnostic accuracy. Experimental results demonstrate that the proposed method significantly improves the accuracy of BUS-to-EUS translation on the MTUSI dataset and that the generated elastography images enhance nodule diagnostic accuracy compared to solely using BUS images on the STUSI and the BUSI datasets. This advancement highlights the potential for broader application of elastography in clinical practice. The code is available at https://github.com/HongchengHan/HSC-T.
期刊介绍:
IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.