StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm.

Irfan, Muhammad; Malik, Khalid Mahmood; Ahmad, Jamil; Malik, Ghaus

Irfan, Muhammad; Malik, Khalid Mahmood; Ahmad, Jamil; Malik, Ghaus.

Afiliação

Irfan M; SMILES LAB, Department of Computer Science and Engineering, Oakland University, Rochester, MI, 48309, USA.
Malik KM; SMILES LAB, Department of Computer Science and Engineering, Oakland University, Rochester, MI, 48309, USA. Electronic address: mahmood@oakland.edu.
Ahmad J; Department of Computer Vision, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates.
Malik G; Executive Vice-Chair at Department of Neurosurgery, Henry Ford Health System, Detroit, MI, USA.

Comput Med Imaging Graph ; 108: 102271, 2023 09.

Article em En | MEDLINE | ID: mdl-37556901

Assuntos

Aneurisma Roto; Aneurisma Intracraniano; Humanos; Aneurisma Intracraniano/diagnóstico por imagem; Aneurisma Roto/diagnóstico por imagem; Aneurisma Roto/complicações; Redes Neurais de Computação; Angiografia Digital/métodos

Palavras-chave

Aneurysm rupture; Computer-aided diagnosis; Features fusion intracranial; Medical image analysis; Prediction segmentation; Subarachnoid Hemorrhage (SAH)

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aneurisma Intracraniano / Aneurisma Roto Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo

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XML

PubMed Links

Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Aneurisma Intracraniano / Aneurisma Roto Idioma: En Ano de publicação: 2023 Tipo de documento: Article