Vessel tractography using an intensity based tensor model

In this paper, we propose a novel tubular structure segmen- tation method, which is based on an intensity-based tensor that fits to a vessel. Our model is initialized with a single seed point and it is ca- pable of capturing whole vessel tree by an automatic branch detection algorithm. The centerline of the vessel as well as its thickness is extracted. We demonstrated the performance of our algorithm on 3 complex contrast varying tubular structured synthetic datasets for quantitative validation. Additionally, extracted arteries from 10 CTA (Computed Tomography An- giography) volumes are qualitatively evaluated by a cardiologist expert’s visual scores

Automated vessel centerline extraction and diameter measurement in OCT Angiography

Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique that enables the visualizationof perfused vasculature in vivo. In ophthalmology,it allows the physician to monitor diseases affecting the vascular networks of the retina such as age-related macular degeneration or diabetic retinopathy. Due to the complexity of the vasculature in the retina,it is of interest to automatically extract vascular parameters which describe the condition of the vessels. Suitable parameters could improve the diagnosis and the treatment during the course of therapy.We present an automated algorithm tocompute the diameters of the vessels in en face OCTA images. After segmentingthe images, the vessel centerlinewascomputed using a thinningalgorithm.The centerline wasrefined by detecting invalid pixelssuch as spursandbycontinuing the centerline until the endsof the vessels. Lastly, the diameter wascomputed by dilating a discrete circle at the position of the centerline or by measuring the distance between both borders of the vessels. The developed algorithms were applied to in vivo images of human eyes. Certainly, no ground truth was available. Hence, a plausibility check was performed by comparing the measured diameters of two different layers of the retina (Superficial Vascular Complex (SVC) and Deep Vascular Complex (DVC)). Each layer exhibits a different characteristic vasculature.The algorithm clearly reflectedthe differences from both retinal layers. The measured diameters demonstrate that the DVC consists of more capillaries and considerably smaller vessels compared to the SVC.The presented method enables automated analysis of the retinal vasculature and forms thereby the basis for monitoringdiseases influencing the vasculature of the retina. The validation of the method using an artificial ground truth is still neede

VTrails: Inferring Vessels with Geodesic Connectivity Trees

The analysis of vessel morphology and connectivity has an impact on a number of cardiovascular and neurovascular applications by providing patient-specific high-level quantitative features such as spatial location, direction and scale. In this paper we present an end-to-end approach to extract an acyclic vascular tree from angiographic data by solving a connectivity-enforcing anisotropic fast marching over a voxel-wise tensor field representing the orientation of the underlying vascular tree. The method is validated using synthetic and real vascular images. We compare VTrails against classical and state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field as proof of concept. VTrails performance is evaluated on images with different levels of degradation: we verify that the extracted vascular network is an acyclic graph (i.e. a tree), and we report the extraction accuracy, precision and recall

Learning to Segment 3D Linear Structures Using Only 2D Annotations

We propose a loss function for training a Deep Neural Network (DNN) to segment volumetric data, that accommodates ground truth annotations of 2D projections of the training volumes, instead of annotations of the 3D volumes themselves. In consequence, we significantly decrease the amount of annotations needed for a given training set. We apply the proposed loss to train DNNs for segmentation of vascular and neural networks in microscopy images and demonstrate only a marginal accuracy loss associated to the significant reduction of the annotation effort. The lower labor cost of deploying DNNs, brought in by our method, can contribute to a wide adoption of these techniques for analysis of 3D images of linear structures