Search Results for author:
Found 8 papers, 1 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.
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.
A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation
This paper highlights a systematic approach to define and quantitatively compare those methods in two different contexts: class-specific epistemic uncertainty maps (one value per image, voxel and class) and combined epistemic uncertainty maps (one value per image and voxel).
Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease
For prediction in MCI, performances decreased for both SVM (0. 665) and CNN (0. 702).
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.
Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning
CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke.
Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
In this paper, we propose a method for automatic segmentation of ICAC; the first to our knowledge.
