Radiomics is a relatively new word for the field of radiology, meaning the extraction of a high
number of quantitative features from medical images. Artificial intelligence (AI) is broadly a …

Clinical imaging (eg, magnetic resonance imaging and computed tomography) is a crucial
adjunct for clinicians, aiding in the diagnosis of diseases and planning of appropriate …

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark
(LiTS), which was organized in conjunction with the IEEE International Symposium on …

Segmentation of livers and liver tumors is one of the most important steps in radiation
therapy of hepatocellular carcinoma. The segmentation task is often done manually, making …

Automatic extraction of liver and tumor from CT volumes is a challenging task due to their
heterogeneous and diffusive shapes. Recently, 2D deep convolutional neural networks …

OBJECTIVE. Radiologic texture is the variation in image intensities within an image and is
an important part of radiomics. The objective of this article is to discuss some parameters …

Rationale and Objectives The automated segmentation of organs and tissues throughout the
body using computed tomography and magnetic resonance imaging has been rapidly …

Due to the gray values of liver and surrounding tissues and organs are resemblance in
abdominal computed tomography (CT) images, it is difficult to accurately determine the …

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 purpose of this research is to demonstrate the ability of machine-learning (ML) methods
for liver cancer classification using a fused dataset of two-dimensional (2D) computed …