51 citations · 161 across the 13 of their papers we have counts for
14 papers
OccluNet: Spatio-Temporal Deep Learning for Occlusion Detection on DSA
Anushka A. Kore, Frank G. te Nijenhuis, Matthijs van der Sluijs +8
Accurate detection of vascular occlusions during endovascular thrombectomy (EVT) is critical in acute ischemic stroke (AIS). Interpretation of digital subtraction angiography (DSA)…
Direct vascular territory segmentation on cerebral digital subtraction angiography
P. Matthijs van der Sluijs, Lotte Strong, Frank G. te Nijenhuis +10
X-ray digital subtraction angiography (DSA) is frequently used when evaluating minimally invasive medical interventions. DSA predominantly visualizes vessels, and soft tissue anato…
CLAIRE-DSA: Fluoroscopic Image Classification for Quality Assurance of Computer Vision Pipelines in Acute Ischemic Stroke
Cristo J. van den Berg, Frank G. te Nijenhuis, Mirre J. Blaauboer +9
Computer vision models can be used to assist during mechanical thrombectomy (MT) for acute ischemic stroke (AIS), but poor image quality often degrades performance. This work prese…
DSCA: A Digital Subtraction Angiography Sequence Dataset and Spatio-Temporal Model for Cerebral Artery Segmentation
Jiong Zhang, Qihang Xie, Lei Mou +6
Cerebrovascular diseases (CVDs) remain a leading cause of global disability and mortality. Digital Subtraction Angiography (DSA) sequences, recognized as the gold standard for diag…
An automated framework for brain vessel centerline extraction from CTA images
Sijie Liu, Ruisheng Su, Jianghang Su +8
Accurate automated extraction of brain vessel centerlines from CTA images plays an important role in diagnosis and therapy of cerebrovascular diseases, such as stroke. However, thi…
An Automatic Cascaded Model for Hemorrhagic Stroke Segmentation and Hemorrhagic Volume Estimation
Weijin Xu, Zhuang Sha, Huihua Yang +4
Hemorrhagic Stroke (HS) has a rapid onset and is a serious condition that poses a great health threat. Promptly and accurately delineating the bleeding region and estimating the vo…
AngioMoCo: Learning-based Motion Correction in Cerebral Digital Subtraction Angiography
Ruisheng Su, Matthijs van der Sluijs, Sandra Cornelissen +6
Cerebral X-ray digital subtraction angiography (DSA) is the standard imaging technique for visualizing blood flow and guiding endovascular treatments. The quality of DSA is often n…
DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms using Self-adversarial Learning
Xin Wang, Tao Tan, Yuan Gao +6
Asymmetry is a crucial characteristic of bilateral mammograms (Bi-MG) when abnormalities are developing. It is widely utilized by radiologists for diagnosis. The question of 'what…
DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences
Wentao Liu, Tong Tian, Lemeng Wang +11
The automated segmentation of Intracranial Arteries (IA) in Digital Subtraction Angiography (DSA) plays a crucial role in the quantification of vascular morphology, significantly c…
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
Carole H. Sudre, Kimberlin Van Wijnen, Florian Dubost +46
Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and…
CAVE: Cerebral Artery-Vein Segmentation in Digital Subtraction Angiography
Ruisheng Su, P. Matthijs van der Sluijs, Yuan Chen +7
Cerebral X-ray digital subtraction angiography (DSA) is a widely used imaging technique in patients with neurovascular disease, allowing for vessel and flow visualization with high…
autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients
Ruisheng Su, Sandra A. P. Cornelissen, Matthijs van der Sluijs +8
The Thrombolysis in Cerebral Infarction (TICI) score is an important metric for reperfusion therapy assessment in acute ischemic stroke. It is commonly used as a technical outcome…
2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small data
Xin Wang, Ruisheng Su, Weiyi Xie +5
In medical-data driven learning, 3D convolutional neural networks (CNNs) have started to show superior performance to 2D CNNs in numerous deep learning tasks, proving the added val…
An Accurate and Transferable Machine-Learning Interatomic Potential for Silicon
Lin Hu, Rui Su, Bing Huang +1
The development of modern ab initio methods has rapidly increased our understanding of physics, chemistry and materials science. Unfortunately, intensive ab initio calculations are…