Abstract
Thyroid nodules are a common endocrine condition that can be detected through medical imaging, aiding in the identification of thyroid cancer. Accurate segmentation of these nodules is crucial for precise diagnosis, considering factors such as size, shape, and number of nodules that influence their grading. Automating the segmentation process can benefit clinicians and researchers by providing efficient and reliable results. However, ultrasound image segmentation presents challenges due to the complex tissue structure surrounding the thyroid. Traditional approaches have relied on manually developed convolutional neural networks (CNNs) based models, which are tedious, error-prone, and require domain-specific expertise. In this paper, an evolutionary neural architecture search (NAS) based method is developed using the Improved Teaching-Learning-Based Optimization (ITLBO) algorithm to discover optimal block structures in the encoder-decoder architecture for thyroid nodule segmentation (TNS) in ultrasound images. The proposed method enables dynamic network structure optimization through a flexible search space. Moreover, attention blocks are incorporated into the encoder-decoder architecture to enhance the performance of segmentation. The proposed method, named EvoThy-Net, is evaluated on two publicly available ultrasound image datasets, demonstrating its potential in discovering superior-performance segmentation networks for the TNS task. The results revealed that the proposed method outperforms other state-of-the-art models.