Enhanced-TransUNet for ultrasound segmentation of thyroid nodules

• Published in: Biomedical signal processing and control 2024-09, Vol.95, p.106472, Article 106472
• Main Authors: Ozcan, Alper, Tosun, Ömür, Donmez, Emrah, Sanwal, Muhammad
• Format: Article
• Language: English
• Subjects: Artificial learning; Deep learning; Image processing; Thyroid nodule segmentation; Ultrasound image segmentation
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2024.106472

Medical image segmentation plays a key role in the early diagnosis and treatment of medical diseases. Thyroid nodule segmentation is a critical step in early thyroid cancer identification. Accurately segmenting thyroid nodule areas from ultrasound images is critical for clinical diagnosis and maintaining good health. Because of the fragile borders of ultrasound images and the complicated structure of thyroid tissue, it is difficult to correctly separate the delicate outlines of thyroid nodules to provide adequate segmentation findings, since they either cannot establish exact edges or segment smaller parts. The segmentation of thyroid nodule images presents some fundamental difficulties. First, the intrinsic locality of convolutional neural network models places constraints on their ability to capture information about the whole context. Second, the size of the data sets used for thyroid nodule segmentation frequently makes overfitting more likely. Finally, low-level characteristics that are important in displaying thyroid borders eventually disappear throughout the feature encoding process. We provide an effective model called Enhanced-TransUNet for thyroid nodule image segmentation to overcome these difficulties. The Transformer and UNet concepts are combined in Enhanced-TransUNet. While the UNet can successfully segregate tiny items, the Transformer can collect information about the overall environment. In order to condense superfluous characteristics and lower the chance of overfitting, Enhanced-TransUNet also makes use of an information bottleneck. Comparing our model to contemporary CNN or UNET based models, experimental findings on the TN3K and DDTI datasets for brain tumor segmentation tasks show that our model gets equivalent or better results. For the two datasets, the average Dice Score and HD95 are 82.92, 95.45, and 13.19, 1.09, respectively. Overall, the Enhanced-TransUNet model for thyroid nodule image segmentation is promising. Even with weak edges and a complicated tissue structure, it can precisely segment thyroid nodules in ultrasound pictures. Due to its usage of the information bottleneck, Enhanced-TransUNet is also less prone to overfitting than other models. As a result, an AI-based decision support system based on this model can be built to reduce workload and misdiagnosis. This system has significant potential for clinical application by radiologists and surgeons, which can increase clinical diagnostic accuracy and efficiency. •A no