The microvascular invasion (MVI) is a major prognostic factor in hepatocellular carcinoma, which one of the malignant tumors with highest mortality rate. diagnosis MVI needs discovering vessels that contain carcinoma cells and counting their number each vessel, depends heavily on experiences doctor, largely subjective time-consuming. However, there no algorithm as yet tailored for detection fro...

Accurate retinal vessel segmentation is an important task for many computer-aided diagnosis systems. Yet, it still a challenging problem due to the complex structures of eye. Numerous methods have been proposed recently, however more research needed deal with poor thin and tiny vessels. To address this, we propose new deep learning pipeline combining efficiency residual dense net blocks and, sq...

The accurate and automatic segmentation of retinal vessels from fundus images is critical for the early diagnosis prevention many eye diseases, such as diabetic retinopathy (DR). Existing vessel approaches based on convolutional neural networks (CNNs) have achieved remarkable effectiveness. Here, we extend a model with low complexity high performance U-Net, which one most popular architectures....

In this paper we propose a Particle Filter-based approach for the segmentation of coronary arteries. To this end, successive planes of the vessel are modeled as unknown states of a sequential process. Such states consist of the orientation, position, shape model and appearance (in statistical terms) of the vessel that are recovered in an incremental fashion, using a sequential Bayesian filter (...

The separation of the retinal vessel network into distinct arterial and venous vessel trees is of high interest. We propose an automated method for identification and separation of retinal vessel trees in a retinal color image by converting a vessel segmentation image into a vessel segment map and identifying the individual vessel trees by graph search. Orientation, width, and intensity of each...

The vasculature is of utmost importance in neurosurgery. Direct visualization of images acquired with current imaging modalities, however, cannot provide a spatial representation of small vessels. These vessels, and their branches which show considerable variations, are most important in planning and performing neurosurgical procedures. In planning they provide information on where the lesion d...

Identification of vascular structures from medical images is integral to many clinical procedures. Most vessel segmentation techniques ignore the characteristic pulsatile motion of vessels in that formulation. In a recent effort to automatically segment vessels that are hidden under fat, we motivated the use of the magnitude of local pulsatile motion extracted from surgical endoscopic video. In...

Segmentation of blood vessels in retinal images allows early diagnosis of disease; automating this process provides several benefits including minimizing subjectivity and eliminating a painstaking, tedious task. Previous approaches, while satisfactory in some cases, still leave room for improvement, especially in abnormal retinal images. We propose to utilize a tracking based algorithm based on...

Coronary heart disease has been one of the main threats to human health. Coronary angiography is taken as the gold standard; for the assessment of coronary artery disease. However, sometimes, the images are difficult to visually interpret because of the crossing and overlapping of vessels in the angiogram. Vessel extraction from X-ray angiograms has been a challenging problem for several years....

We present a segmentation method for vessels using an implicit deformable model with a soft shape prior. Blood vessels are challenging structures to segment due to their branching and thinning geometry as well as the decrease in image contrast from the root of the vessel to its thin branches. Using image intensity alone to deform a model for the task of segmentation often results in leakages at...