Accurate segmentation of medical ultrasound images is crucial for guiding treatment
decisions and assessing intervention effectiveness. The challenge of segmenting lesions in …

Segmenting ultrasound images is critical for various medical applications, but it offers
significant challenges due to ultrasound images' inherent noise and unpredictability. To …

Ultrasound image segmentation is a challenging task due to the complexity of lesion types,
fuzzy boundaries, and low-contrast images along with the presence of noises and artifacts …

Most ultrasound image segmentation methods employ a joint training strategy, where the
model learns the edge and core regions indiscriminately and uniformly. However, they often …

In this paper, we propose a novel convolutional neural network (MDR-Net) for ultrasound
image segmentation by exploiting multi-decision and deep refinement of the target. Our …

Ultrasound image segmentation research is of great significance, especially in medical
diagnosis and clinical treatment. However, speckle noise caused by reflections from different …

Ultrasound imaging, as a portable and radiation-free modality, presents challenges for
accurate segmentation due to the variability of lesions and the similar intensity values of …

Ultrasound imaging is one of the most widely used medical imaging techniques for
visualizing human tissue due to its economical, convenient, practical, and safe advantages …

Ultrasound imaging, characterized by low contrast, high noise, and interference from
surrounding tissues, poses significant challenges in lesion segmentation. To tackle these …

Automatic and accurate segmentation of medical images is an important task due to the
direct impact of this procedure on both disease diagnosis and treatment. Segmentation of …