Deep segmentation models that generalize to images with unknown appearance are
important for real-world medical image analysis. Retraining models leads to high latency …

Deep Neural Networks (DNNs) suffer from the performance degradation when image
appearance shift occurs, especially in ultrasound (US) image segmentation. In this paper …

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 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 can vary in style/appearance due to differences in scanning equipment
and other factors, resulting in degraded segmentation and classification performance of …

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

The highly ambiguous nature of boundaries and similar objects is difficult to address in
some ultrasound image segmentation tasks, such as neck muscle segmentation, leading to …

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 plays a vital role in the early diagnosis of human diseases.
It helps diagnose many diseases, such as breast cancer, hemangioma, and other …

Segmentation of ultrasound images is an essential task in both diagnosis and image-guided
interventions given the ease-of-use and low cost of this imaging modality. As manual …