Percutaneous thermal ablation has proven to be an effective modality for treating both
benign and malignant tumours in various tissues. Among these modalities, radiofrequency …

The segmentation of liver blood vessels is of major importance as it is essential for
formulating diagnoses, planning and delivering treatments, as well as evaluating the results …

Liver vessel segmentation is fast becoming a key instrument in the diagnosis and surgical
planning of liver diseases. In clinical practice, liver vessels are normally manual annotated …

Simple Summary The classification is performed later by an interactively learning Swin
Transformer block, the core unit for feature representation and long-range semantic …

Purpose To evaluate the performance of a deep learning (DL) model that measures the liver
segmental volume ratio (LSVR)(ie, the volumes of Couinaud segments I–III/IV–VIII) and …

Background Both early detection and severity assessment of liver trauma are critical for
optimal triage and management of trauma patients. Current trauma protocols utilize …

Purpose: Developing an MRI-based radiomics model to effectively and accurately predict
the predominant histopathologic growth patterns (HGPs) of colorectal liver metastases …

We evaluated the effectiveness of automated segmentation of the liver and its vessels with a
convolutional neural network on non-contrast T1 vibe Dixon acquisitions. A dataset of non …

Liver cancer is one of the most common malignant diseases worldwide. Segmentation and
reconstruction of liver tumors and vessels in CT images can provide convenience for …

Automatic liver tumor segmentation is of great importance for assisting doctors in liver
cancer diagnosis and treatment planning. Recently, deep learning approaches trained with …