We present a method for automatically segmenting the blood vessels in optic nerve head (ONH) centered spectral-domain optical coherence tomography (SD-OCT) volumes, with a focus on the ability to segment the vessels in the region near the neural canal opening (NCO). The algorithm first pre-segments the NCO using a graph-theoretic approach. Oriented Gabor wavelets rotated around the center of th...

INTRODUCTION Non-invasive quantitative assessment of the cerebral vasculature is of high diagnostic and therapeutic interest. Prerequisite for quantitative description of blood vessels is voxel-wise classification of angiographic data sets into vessel and non-vessel structures. Among the vast of algorithms suitable for vessel segmentation [1, 2] the level-set technique has been established as a...

Accurate quantification of calcium deposit is an important task in assessing coronary artery disease, especially when the investigation of the disease progress is considered. The reproducibility and robustness of the segmentation algorithm against partial volume effect and noise is critical for an accurate quantification. While there are several approaches for segmentation of the volume of the ...

In this paper we present a robust skull-stripping method for the isolation of cerebral tissue in 3D Time-of-Flight (TOF) magnetic resonance angiographic images of the brain. 3D TOF images are often acquired in case of cerebral vascular diseases, because of their good blood-to-background-contrast. Skull-stripping is an essential preprocessing step towards a better segmentation as well as direct ...

Automatic segmentation of blood vessels in fundus images is of great importance as eye diseases as well as some systemic diseases cause observable pathologic modifications. It is a binary classification problem: for each pixel we consider two possible classes (vessel or non-vessel). We use a GPU implementation of deep max-pooling convolutional neural networks to segment blood vessels. We test o...