Automatic Detection of Extra-Cardiac Findings in Cardiovascular Magnetic Resonance

Autor:	Pier Giorgio Masci, Andrew P. King, Natallia Khenkina, Esther Puyol-Antón, Dewmini Hasara Wickremasinghe
Rok vydání:	2021
Předmět:	medicine.medical_specialty medicine.diagnostic_test Binary classification business.industry Internal medicine Cardiovascular structure Cardiology Medicine Magnetic resonance imaging Precordial examination business
Zdroj:	Medical Image Understanding and Analysis ISBN: 9783030804312 MIUA
DOI:	10.1007/978-3-030-80432-9_8
Popis:	Cardiovascular magnetic resonance (CMR) is an established, non-invasive technique to comprehensively assess cardiovascular structure and function in a variety of acquired and inherited cardiac conditions. In addition to the heart, a typical CMR examination will also image adjacent thoracic and abdominal structures. Consequently, findings incidental to the cardiac examination may be encountered, some of which may be clinically relevant. We compare two deep learning architectures to automatically detect extra cardiac findings (ECFs) in the HASTE sequence of a CMR acquisition. The first one consists of a binary classification network that detects the presence of ECFs and the second one is a multi-label classification network that detects and classifies the type of ECF. We validated the two models on a cohort of 236 subjects, corresponding to 5610 slices, where 746 ECFs were found. Results show that the proposed methods have promising balanced accuracy and sensitivity and high specificity.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::19de837de54bd7a11203f25cd52a2089 https://doi.org/10.1007/978-3-030-80432-9_8 Zobrazit plný text záznamu