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Found 10 papers, 3 papers with code
An automated framework for brain vessel centerline extraction from CTA images
In this paper, we consider automatic lumen segmentation generation without additional annotation effort by physicians and more effective use of the generated lumen segmentation for improved centerline extraction performance.
AngioMoCo: Learning-based Motion Correction in Cerebral Digital Subtraction Angiography
This hinders the serial evaluation of blood flow, and often causes undesired vasculature and contrast flow alterations, leading to impeded usability in clinical practice.
Comparison of retinal regions-of-interest imaged by OCT for the classification of intermediate AMD
In addition to the six ROIs, the whole OCT B-scan and the binary mask corresponding to the segmentation of the RPE-BM complex were used.
Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentation
In this paper, we propose a novel label refinement method to correct such errors from an initial segmentation, implicitly incorporating information about label structure.
CAVE: Cerebral Artery-Vein Segmentation in Digital Subtraction Angiography
Further, it falls short in effectively separating arteries and veins as it disregards the temporal perspectives inherent in DSA.
Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review
The automatic segmentation of optic nerve head in OCT scans could further improve the current clinical management of glaucoma and other diseases.
autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients
Finally, we quantify the autoTICI score as the ratio of reperfused pixels after EVT.
Deep learning-based retinal vessel segmentation with cross-modal evaluation
The main goal is to analyse if a model trained in one of two modalities, Fundus Photography (FP) or Scanning Laser Ophthalmoscopy (SLO), is transferable to the other modality accurately.
Dynamic Coronary Roadmapping via Catheter Tip Tracking in X-ray Fluoroscopy with Deep Learning Based Bayesian Filtering
The proposed roadmapping and tracking approaches were validated on clinical X-ray images, achieving accurate performance on both catheter tip tracking and dynamic coronary roadmapping experiments.
Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy
We trained the method on images from 182 X-ray sequences from 23 different interventions.
