Objective: Three-dimensional (3D) blood vessel structure information is important for
diagnosis and treatment in various clinical scenarios. We present a fully automatic method …

In this paper we propose a fully automatic 2-stage cascaded approach for segmentation of
liver and its tumors in CT (Computed Tomography) images using densely connected fully …

Deep learning has become a thriving force in the computer aided diagnosis of liver cancer,
as it solves extremely complicated challenges with high accuracy over time and facilitates …

Accurate segmentation of specific organ from computed tomography (CT) scans is a basic
and crucial task for accurate diagnosis and treatment. To avoid time-consuming manual …

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 …

Deep Learning, refers to large set of neural network based algorithms, have emerged as
promising machine-learning tools in the general imaging and computer vision domains …

With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …

Abdominal anatomy segmentation is crucial for numerous applications from computer-
assisted diagnosis to image-guided surgery. In this context, we address fully-automated …

Dense segmentation of large medical image volumes using a labelled training dataset
requires strong classifiers. Ensembles of random decision trees have been shown to …

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer in adults,
and the most common cause of death of people suffering from cirrhosis. The segmentation of …