Purpose: This paper proposes a new network framework called EAR-U-Net, which
leverages EfficientNetB4, attention gate, and residual learning techniques to achieve …

This paper proposes a new network framework, which leverages EfficientNetB4, attention
gate, and residual learning techniques to achieve automatic and accurate liver …

Due to low contrast and the blurred boundary between liver tissue and neighboring organs
sharing similar intensity values, the problem of liver segmentation from CT images has not …

Automatic liver segmentation from CT images is challenging due to the indistinct boundaries
between the liver and surrounding organs in the abdominal cavity CT. To address these …

Abdominal computed tomography (CT) is a common modality to detect liver lesions. Liver
segmentation in CT scan is important for diagnosis and analysis of liver lesions. However …

Liver cancer is one of the cancers with the highest mortality. In order to help doctors
diagnose and treat liver lesion, an automatic liver segmentation model is urgently needed …

Background and objective Liver segmentation is an essential prerequisite for liver cancer
diagnosis and surgical planning. Traditionally, liver contour is delineated manually by …

Background Automatic liver segmentation is a prerequisite for hepatoma treatment;
however, the low accuracy and stability hinder its clinical application. To alleviate this …

Liver segmentation is a fundamental prerequisite for the diagnosis and surgical planning of
hepatocellular carcinoma. Traditionally, the liver contour is drawn manually by radiologists …

Background The liver is an important organ that undertakes the metabolic function of the
human body. Liver cancer has become one of the cancers with the highest mortality. In …