利用DC-Contrast U-Net增强小儿甲状腺超声图像分割。

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 2024-10-11 DOI:10.1186/s12880-024-01415-0
Bo Peng, Wu Lin, Wenjun Zhou, Yan Bai, Anguo Luo, Shenghua Xie, Lixue Yin
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引用次数: 0

摘要

甲状腺的早期筛查方法包括触诊和成像。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症状方面的效果可能有限,尤其是对儿童而言。因此,这是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。在目前的临床实践中,准确性取决于超声波操作员的经验,从而导致主观结果。即使是专家,在甲状腺识别方面也很难达成一致。此外,在目前的临床实践中,超声波机的有效使用也依赖于超声波操作员的经验。为了从小儿甲状腺超声图像中提取足够的纹理信息,同时降低计算复杂度和参数数量,本文设计了一种基于 U-Net 的新型网络,称为 DC-Contrast U-Net,旨在以较低的复杂度在医学图像分割中实现更好的分割性能。研究结果表明,与其他 U-Net 相关的分割模型相比,本文提出的 DC-Contrast U-Net 模型在提高推理速度的同时,还获得了更高的分割精度,有望在未来的临床应用中部署到医疗边缘设备中。
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Enhanced pediatric thyroid ultrasound image segmentation using DC-Contrast U-Net.

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.

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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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