The automatic segmentation of blood vessels in fundus images can help analyze the condition retinal vasculature, which is crucial for identifying various systemic diseases like hypertension, diabetes, etc. Despite success Deep Learning-based models this task, most them are heavily parametrized and thus have limited use practical applications. This paper proposes IterMiUnet, a new lightweight co...

Most existing deep learning based methods for vessel segmentation neglect two important aspects of retinal vessels: The orientation information vessels and the contextual whole fundus region. In this paper, we propose a robust context entangled network (OCE-Net), which can extract complex from blood vessels. To achieve orientation-aware convolution, dynamic convolution (DCOA Conv) to with multi...

Retinal vessel segmentation is a fundamental step in screening, diagnosis, and treatment of various cardiovascular ophthalmic diseases. Robustness one the most critical requirements for practical utilization, since test images may be captured using different fundus cameras, or affected by pathological changes. We investigate this problem from data augmentation perspective, with merits no additi...

Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging modality that allows micron-level resolution to visualize the retinal microvasculature. The vessel segmentation in OCTA images still an open problem, and especially thin dense structure of capillary plexus important challenge this problem. In work, we propose image magnification network (IMN) for images. Contrary U-...

Accurate blood vessel segmentation plays a crucial role in non-invasive blood flow velocity measurement based on complex-valued magnetic resonance images. We propose a specific snake active contour model-based blood vessel segmentation framework for complex-valued magnetic resonance images. The proposed framework combines both magnitude and phase information from a complex-valued image represen...

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 ...

Ocular fundus image assessment has been extensively used by ophthalmologists for diagnosing vascular and non vascular pathology. Examining the retinal blood vessel network may reveal arteriosclerosis, diabetes, hypertension, cardiovascular disease and stroke [12]. Furthermore, the segmentation of the vessel network is the most suitable representation for the retinal image registration since vas...

Retinal blood flow quantification by retinal vessel segmentation with Doppler optical coherence angiography is presented. Vessel diameter, orientation, and position are determined in an en face vessel image and two representative cross-sectional flow images of the vessel. Absolute blood flow velocity is calculated with the help of the measured Doppler frequency shift and determined vessel angle...