Search Results for author:
Found 10 papers, 2 papers with code
UDCR: Unsupervised Aortic DSA/CTA Rigid Registration Using Deep Reinforcement Learning and Overlap Degree Calculation
In this paper, we propose an unsupervised method, UDCR, for aortic DSA/CTA rigid registration based on deep reinforcement learning.
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.
An Automatic Cascaded Model for Hemorrhagic Stroke Segmentation and Hemorrhagic Volume Estimation
Hemorrhagic Stroke (HS) has a rapid onset and is a serious condition that poses a great health threat.
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.
DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms using Self-adversarial Learning
The question of 'what the symmetrical Bi-MG would look like when the asymmetrical abnormalities have been removed ?'
DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences
In this paper, we introduces DIAS, a dataset specifically developed for IA segmentation in DSA sequences.
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels.
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.
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.
2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small data
Such performance gain is more pronounced with transfer learning or in the case of limited training data.
