Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model-Reference-Cited by-同舟云学术

Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model

Published:2023-05-18 Issue:6 Volume:44 Page:641-648
ISSN:0195-6108
Container-title:American Journal of Neuroradiology
language:en
Short-container-title:AJNR Am J Neuroradiol

Author:

Zhu K.^ORCID,Bala F.^ORCID,Zhang J.^ORCID,Benali F.^ORCID,Cimflova P.^ORCID,Kim B.J.^ORCID,McDonough R.^ORCID,Singh N.^ORCID,Hill M.D.^ORCID,Goyal M.^ORCID,Demchuk A.^ORCID,Menon B.K.^ORCID,Qiu W.^ORCID

Funder

Distinguished Young Scholars of the National Natural Science Foundation of China

Natural Science Basic Research Plan in Shaanxi Province of China

National key research and development program

Publisher

American Society of Neuroradiology (ASNR)

Subject

Neurology (clinical),Radiology, Nuclear Medicine and imaging

Reference35 articles.

1. Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials

2. Efficacy of endovascular thrombectomy in patients with M2 segment middle cerebral artery occlusions: meta-analysis of data from the HERMES Collaboration

3. Association of Clinical, Imaging, and Thrombus Characteristics With Recanalization of Visible Intracranial Occlusion in Patients With Acute Ischemic Stroke

4. The Importance of Size

5. Radiomics-Based Intracranial Thrombus Features on CT and CTA Predict Recanalization with Intravenous Alteplase in Patients with Acute Ischemic Stroke

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Detection and segmentation of hyperdense middle cerebral artery sign on non-contrast CT using artificial intelligence;2024-07-27

2. Value of Automatically Derived Full Thrombus Characteristics: An Explorative Study of Their Associations with Outcomes in Ischemic Stroke Patients;Journal of Clinical Medicine;2024-02-28

3. Principes fondamentaux de l’apprentissage automatique pour les neurologues;Pratique Neurologique - FMC;2023-12

4. Deep-Learning-Modell zur automatischen Segmentierung intrakranieller Thromben;Neuroradiologie Scan;2023-10

5. Regarding “Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model”;American Journal of Neuroradiology;2023-08-17