Ultrasound in Cardiac Electrophysiology and Biomechanics Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China.
School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu, 610500, China.
BMC Med Imaging. 2024 Oct 11;24(1):275. doi: 10.1186/s12880-024-01415-0.
Early screening methods for the thyroid gland include palpation and imaging. Although palpation is relatively simple, its effectiveness in detecting early clinical signs of the thyroid gland may be limited, especially in children, due to the shorter thyroid growth time. Therefore, this constitutes a crucial foundational work. However, accurately determining the location and size of the thyroid gland in children is a challenging task. Accuracy depends on the experience of the ultrasound operator in current clinical practice, leading to subjective results. Even among experts, there is poor agreement on thyroid identification. In addition, the effective use of ultrasound machines also relies on the experience of the ultrasound operator in current clinical practice. In order to extract sufficient texture information from pediatric thyroid ultrasound images while reducing the computational complexity and number of parameters, this paper designs a novel U-Net-based network called DC-Contrast U-Net, which aims to achieve better segmentation performance with lower complexity in medical image segmentation. The results show that compared with other U-Net-related segmentation models, the proposed DC-Contrast U-Net model achieves higher segmentation accuracy while improving the inference speed, making it a promising candidate for deployment in medical edge devices in clinical applications in the future.
早期甲状腺筛查方法包括触诊和影像学检查。虽然触诊相对简单，但由于甲状腺生长时间较短，其在检测甲状腺早期临床征象方面的有效性可能有限，尤其是在儿童中。因此，这构成了一项至关重要的基础工作。然而，准确确定儿童甲状腺的位置和大小是一项具有挑战性的任务。准确性取决于当前临床实践中超声操作人员的经验，导致结果具有主观性。即使是专家之间，对甲状腺的识别也存在较差的一致性。此外，超声仪器的有效使用也依赖于当前临床实践中超声操作人员的经验。为了在降低计算复杂度和参数数量的同时从儿科甲状腺超声图像中提取足够的纹理信息，本文设计了一种名为 DC-Contrast U-Net 的新型基于 U-Net 的网络，旨在实现更好的分割性能和更低的复杂性在医学图像分割中。结果表明，与其他基于 U-Net 的分割模型相比，所提出的 DC-Contrast U-Net 模型在提高推断速度的同时实现了更高的分割准确性，使其成为未来在医学边缘设备中部署的有前途的候选者，可应用于临床应用。
