This paper reviews ultrasound segmentation methods, in a broad sense, focusing on
techniques developed for medical B-mode ultrasound images. First, we present a review of …

Purpose Breast cancer is the most common form of cancer among women worldwide.
Ultrasound imaging is one of the most frequently used diagnostic tools to detect and classify …

Deep learning methods, especially convolutional neural networks, have been successfully
applied to lesion segmentation in breast ultrasound (BUS) images. However, pattern …

Automatic breast lesion segmentation in ultrasound helps to diagnose breast cancer, which
is one of the dreadful diseases that affect women globally. Segmenting breast regions …

Breast cancer is a great threat to females. Ultrasound imaging has been applied extensively
in diagnosis of breast cancer. Due to the poor image quality, segmentation of breast …

Incorporating human domain knowledge for breast tumor diagnosis is challenging because
shape, boundary, curvature, intensity or other common medical priors vary significantly …

Breast cancer is one of the leading causes of cancer death among women worldwide. In
clinical routine, automatic breast ultrasound (BUS) image segmentation is very challenging …

This study aims to build a deep learning (DL) architecture for automated extraction of
learned-from-data image features from the shear-wave elastography (SWE), and to evaluate …

Background and objective Breast lesions segmentation is an important step of computer-
aided diagnosis system. However, speckle noise, heterogeneous structure, and similar …

Purpose Due to the low contrast, blurry boundaries, and large amount of shadows in breast
ultrasound (BUS) images, automatic tumor segmentation remains a challenging task. Deep …