Objective: Accurate segmentation of liver tumors, which could help physicians make
appropriate treatment decisions and assess the effectiveness of surgical treatment, is crucial …

Automatic liver tumor segmentation can facilitate the planning of liver interventions. For
diagnosis of hepatocellular carcinoma, dynamic contrast-enhanced MRI (DCE-MRI) can …

Automated segmentation of liver tumors in contrast-enhanced abdominal computed
tomography (CT) scans is essential in assisting medical professionals to evaluate tumor …

Liver lesion segmentation is a difficult yet critical task for medical image analysis. Recently,
deep learning based image segmentation methods have achieved promising performance …

Objective Radiomic and deep learning studies based on magnetic resonance imaging (MRI)
of liver tumor are gradually increasing. Manual segmentation of normal hepatic tissue and …

Liver tumours are diseases with high morbidity and high deterioration probabilities, and
accurate liver area segmentation from computed tomography (CT) scans is a prerequisite for …

Liver tumor segmentation plays an essential role in diagnosis and treatment of
hepatocellular carcinoma or metastasis. However, accurate and automatic tumor …

Automatic liver segmentation gained significant attention in the medical realm to deal with
liver anomalies. Furthermore, due to advancements in medical imaging, data volume is …

Developing an effective liver and liver tumor segmentation model from CT scans is very
important for the success of liver cancer diagnosis, surgical planning and cancer treatment …

Background Clinical histological grading of hepatocellular carcinoma (HCC) differentiation
is of great significance in clinical diagnoses, treatments, and prognoses. However, it is …