Ultrasound image segmentation: A systematic review of deformable models from classical techniques to intelligent advancements

Abstract

Ultrasound imaging is a widely used diagnostic modality in modern medicine due to its affordability, safety, and real-time functionality, which eliminates the need for radiation exposure. However, low contrast, speckle noise, and imaging artifacts often limit its effectiveness, making accurate interpretation and analysis challenging. This highlights the need for advanced segmentation techniques to extract clinically meaningful information. Deformable models have emerged as reliable solutions for ultrasound image segmentation, as they effectively capture complex anatomical structures with mathematical stability and adaptability. This review systematically explores the development and application of deformable models and hybrid approaches that integrate edge-region-based methods, statistical techniques, and deep learning strategies. We critically analyze recent advances, compare various models across multiple datasets and clinical contexts, and discuss their strengths and limitations. The review highlights that synergistic edge-region hybrid models tend to offer higher segmentation accuracy, while deep learning-based hybrid models provide the advantage of automation. Despite these advancements, most models still struggle with noisy and low-contrast images, indicating the need for more robust, adaptive, and computationally efficient solutions for real-world clinical use.