Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Rasoulian, Amirhossein"'
Intracranial hemorrhage (ICH) is a life-threatening condition that requires rapid and accurate diagnosis to improve treatment outcomes and patient survival rates. Recent advancements in supervised deep learning have greatly improved the analysis of m
Externí odkaz:
http://arxiv.org/abs/2407.20461
Recent rising interests in patient-specific thoracic surgical planning and simulation require efficient and robust creation of digital anatomical models from automatic medical image segmentation algorithms. Deep learning (DL) is now state-of-the-art
Externí odkaz:
http://arxiv.org/abs/2402.03230
Early surgical treatment of brain tumors is crucial in reducing patient mortality rates. However, brain tissue deformation (called brain shift) occurs during the surgery, rendering pre-operative images invalid. As a cost-effective and portable tool,
Externí odkaz:
http://arxiv.org/abs/2308.10784
Accurate identification and quantification of unruptured intracranial aneurysms (UIAs) is crucial for the risk assessment and treatment of this cerebrovascular disorder. Current 2D manual assessment on 3D magnetic resonance angiography (MRA) is subop
Externí odkaz:
http://arxiv.org/abs/2308.03001
Homologous anatomical landmarks between medical scans are instrumental in quantitative assessment of image registration quality in various clinical applications, such as MRI-ultrasound registration for tissue shift correction in ultrasound-guided bra
Externí odkaz:
http://arxiv.org/abs/2307.14523
In brain tumor resection, accurate removal of cancerous tissues while preserving eloquent regions is crucial to the safety and outcomes of the treatment. However, intra-operative tissue deformation (called brain shift) can move the surgical target an
Externí odkaz:
http://arxiv.org/abs/2307.14520
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2023)
Intracranial hemorrhage (ICH) is a life-threatening medical emergency that requires timely and accurate diagnosis for effective treatment and improved patient survival rates. While deep learning techniques have emerged as the leading approach for med
Externí odkaz:
http://arxiv.org/abs/2304.04902
Autor:
Salari, Soorena, Rasoulian, Amirhossein, Battie, Michele, Fortin, Maryse, Rivaz, Hassan, Xiao, Yiming
Publikováno v:
Proceedings of SPIE; 3/8/2023, Vol. 12464, p1246414-1246414, 1p
Autor:
Colliot, Olivier, Išgum, Ivana, Salari, Soorena, Rasoulian, Amirhossein, Battie, Michele, Fortin, Maryse, Rivaz, Hassan, Xiao, Yiming
Publikováno v:
Proceedings of SPIE; April 2023, Vol. 12464 Issue: 1 p1246414-1246414-7