An anomaly detection approach to identify chronic brain infarcts on MRI

Abstract The performance of current machine learning methods to detect heterogeneous pathology is limited by the quantity and quality of pathology in medical images. A possible solution is anomaly detection; an approach that can detect all abnormalities by learning how ‘normal’ tissue looks like. In...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Kees M. van Hespen, Jaco J. M. Zwanenburg, Jan W. Dankbaar, Mirjam I. Geerlings, Jeroen Hendrikse, Hugo J. Kuijf
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5bc99581278d428aa446e930a860e604
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5bc99581278d428aa446e930a860e604
record_format	dspace
spelling	oai:doaj.org-article:5bc99581278d428aa446e930a860e6042021-12-02T14:37:39ZAn anomaly detection approach to identify chronic brain infarcts on MRI10.1038/s41598-021-87013-42045-2322https://doaj.org/article/5bc99581278d428aa446e930a860e6042021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87013-4https://doaj.org/toc/2045-2322Abstract The performance of current machine learning methods to detect heterogeneous pathology is limited by the quantity and quality of pathology in medical images. A possible solution is anomaly detection; an approach that can detect all abnormalities by learning how ‘normal’ tissue looks like. In this work, we propose an anomaly detection method using a neural network architecture for the detection of chronic brain infarcts on brain MR images. The neural network was trained to learn the visual appearance of normal appearing brains of 697 patients. We evaluated its performance on the detection of chronic brain infarcts in 225 patients, which were previously labeled. Our proposed method detected 374 chronic brain infarcts (68% of the total amount of brain infarcts) which represented 97.5% of the total infarct volume. Additionally, 26 new brain infarcts were identified that were originally missed by the radiologist during radiological reading. Our proposed method also detected white matter hyperintensities, anomalous calcifications, and imaging artefacts. This work shows that anomaly detection is a powerful approach for the detection of multiple brain abnormalities, and can potentially be used to improve the radiological workflow efficiency by guiding radiologists to brain anomalies which otherwise remain unnoticed.Kees M. van HespenJaco J. M. ZwanenburgJan W. DankbaarMirjam I. GeerlingsJeroen HendrikseHugo J. KuijfNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kees M. van Hespen Jaco J. M. Zwanenburg Jan W. Dankbaar Mirjam I. Geerlings Jeroen Hendrikse Hugo J. Kuijf An anomaly detection approach to identify chronic brain infarcts on MRI
description	Abstract The performance of current machine learning methods to detect heterogeneous pathology is limited by the quantity and quality of pathology in medical images. A possible solution is anomaly detection; an approach that can detect all abnormalities by learning how ‘normal’ tissue looks like. In this work, we propose an anomaly detection method using a neural network architecture for the detection of chronic brain infarcts on brain MR images. The neural network was trained to learn the visual appearance of normal appearing brains of 697 patients. We evaluated its performance on the detection of chronic brain infarcts in 225 patients, which were previously labeled. Our proposed method detected 374 chronic brain infarcts (68% of the total amount of brain infarcts) which represented 97.5% of the total infarct volume. Additionally, 26 new brain infarcts were identified that were originally missed by the radiologist during radiological reading. Our proposed method also detected white matter hyperintensities, anomalous calcifications, and imaging artefacts. This work shows that anomaly detection is a powerful approach for the detection of multiple brain abnormalities, and can potentially be used to improve the radiological workflow efficiency by guiding radiologists to brain anomalies which otherwise remain unnoticed.
format	article
author	Kees M. van Hespen Jaco J. M. Zwanenburg Jan W. Dankbaar Mirjam I. Geerlings Jeroen Hendrikse Hugo J. Kuijf
author_facet	Kees M. van Hespen Jaco J. M. Zwanenburg Jan W. Dankbaar Mirjam I. Geerlings Jeroen Hendrikse Hugo J. Kuijf
author_sort	Kees M. van Hespen
title	An anomaly detection approach to identify chronic brain infarcts on MRI
title_short	An anomaly detection approach to identify chronic brain infarcts on MRI
title_full	An anomaly detection approach to identify chronic brain infarcts on MRI
title_fullStr	An anomaly detection approach to identify chronic brain infarcts on MRI
title_full_unstemmed	An anomaly detection approach to identify chronic brain infarcts on MRI
title_sort	anomaly detection approach to identify chronic brain infarcts on mri
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5bc99581278d428aa446e930a860e604
work_keys_str_mv	AT keesmvanhespen ananomalydetectionapproachtoidentifychronicbraininfarctsonmri AT jacojmzwanenburg ananomalydetectionapproachtoidentifychronicbraininfarctsonmri AT janwdankbaar ananomalydetectionapproachtoidentifychronicbraininfarctsonmri AT mirjamigeerlings ananomalydetectionapproachtoidentifychronicbraininfarctsonmri AT jeroenhendrikse ananomalydetectionapproachtoidentifychronicbraininfarctsonmri AT hugojkuijf ananomalydetectionapproachtoidentifychronicbraininfarctsonmri AT keesmvanhespen anomalydetectionapproachtoidentifychronicbraininfarctsonmri AT jacojmzwanenburg anomalydetectionapproachtoidentifychronicbraininfarctsonmri AT janwdankbaar anomalydetectionapproachtoidentifychronicbraininfarctsonmri AT mirjamigeerlings anomalydetectionapproachtoidentifychronicbraininfarctsonmri AT jeroenhendrikse anomalydetectionapproachtoidentifychronicbraininfarctsonmri AT hugojkuijf anomalydetectionapproachtoidentifychronicbraininfarctsonmri
_version_	1718391007218761728

An anomaly detection approach to identify chronic brain infarcts on MRI

Ejemplares similares