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

Background Proper segmentation of the liver from medical images is critical for computer-
assisted diagnosis, therapy and surgical planning. Knowledge of its vascular structure …

Primary tumors have a high likelihood of developing metastases in the liver, and early
detection of these metastases is crucial for patient outcome. We propose a method based on …

Developing methods to segment the liver in medical images, study and analyze it remains a
significant challenge. The shape of the liver can vary considerably from one patient to …

Hepatocellular carcinoma (HCC) stands as the second most prevalent cancer and a leading
cause of cancer-related mortality globally, necessitating precise diagnostic and prognostic …

This paper presents a novel framework devoted to the detection of HCC (Hepato-Cellular
Carcinoma) within hepatic DCE-MRI (Dynamic Contrast-Enhanced MRI) sequences, by a …

Liver tumor semantic segmentation is a crucial task in medical image analysis that requires
multiple MRI modalities. This paper proposes a novel coarse-to-fine fusion segmentation …

Reading and interpreting the medical image still remains the most challenging task in
radiology. Through the important achievement of deep Convolutional Neural Networks …

This work proposes a deep learning algorithm based on the Convolutional Neural Network
(CNN) architecture to detect HepatoCellular Carcinoma (HCC) from liver DCE-MRI …

The glomerular filtration rate determination is the principal method to determine a patient's
chronic kidney disease stage. Currently, there are two main methods for its determination …