Enhanced pediatric thyroid ultrasound image segmentation using DC-Contrast U-Net

• Published in: BMC medical imaging Vol. 24; no. 1; pp. 275 - 19
• Main Authors: Peng, Bo, Lin, Wu, Zhou, Wenjun, Bai, Yan, Luo, Anguo, Xie, Shenghua, Yin, Lixue
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 11.10.2024; BioMed Central; BMC
• Subjects: Accuracy; Adolescent; Adults; Algorithms; Child; Child, Preschool; Clinical medicine; Complexity; DC-Contrast U-Net; Deep learning; Effectiveness; Esophagus; Female; Hormones; Humans; Image contrast; Image enhancement; Image Interpretation, Computer-Assisted - methods; Image processing; Image segmentation; Infant; Information processing; Male; Medical imaging; Medical imaging equipment; Methods; Neural Networks, Computer; Parameter identification; Pediatrics; Radiation; Segmentation; Thyroid; Thyroid diseases; Thyroid gland; Thyroid Gland - diagnostic imaging; Ultrasonic imaging; Ultrasonography - methods; Ultrasound; Ultrasound images; Pediatrics; Ultrasound images; Segmentation; DC-Contrast U-Net; Thyroid
• ISSN: 1471-2342; 1471-2342
• 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.