Universität zu Köln

Braeker, Norbert, Schmitz, Cornelia, Wagner, Natalie, Stanicki, Badrudin J., Schroeder, Christina, Ehret, Felix ORCID: 0000-0001-6177-1755, Fuerweger, Christoph, Zwahlen, Daniel R., Foerster, Robert ORCID: 0000-0002-7664-9207, Muacevic, Alexander and Windisch, Paul (2022). Classifying the Acquisition Sequence for Brain MRIs Using Neural Networks on Single Slices. Cureus J Med Sci, 14 (2). PALO ALTO: CUREUS INC. ISSN 2168-8184

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Abstract

Background Neural networks for analyzing MRIs are oftentimes trained on particular combinations of perspectives and acquisition sequences. Since real-world data are less structured and do not follow a standard denomination of acquisition sequences, this impedes the transition from deep learning research to clinical application. The purpose of this study is therefore to assess the feasibility of classifying the acquisition sequence from a single MRI slice using convolutional neural networks. Methods A total of 113 MRI slices from 52 patients were used in a transfer learning approach to train three convolutional neural networks of different complexities to predict the acquisition sequence, while 27 slices were used for internal validation. The model then underwent external validation on 600 slices from 273 patients belonging to one of four classes (T1-weighted without contrast enhancement, T1-weighted with contrast enhancement, T2-weighted, and diffusion-weighted). Categorical accuracy was noted, and the results of the predictions for the validation set are provided with confusion matrices. Results The neural networks achieved a categorical accuracy of 0.79, 0.81, and 0.84 on the external validation data. The implementation of Grad-CAM showed no clear pattern of focus except for T2-weighted slices, where the network focused on areas containing cerebrospinal fluid. Conclusion Automatically classifying the acquisition sequence using neural networks seems feasible and could be used to facilitate the automatic labelling of MRI data.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Braeker, Norbert UNSPECIFIED UNSPECIFIED UNSPECIFIED Schmitz, Cornelia UNSPECIFIED UNSPECIFIED UNSPECIFIED Wagner, Natalie UNSPECIFIED UNSPECIFIED UNSPECIFIED Stanicki, Badrudin J. UNSPECIFIED UNSPECIFIED UNSPECIFIED Schroeder, Christina UNSPECIFIED UNSPECIFIED UNSPECIFIED Ehret, Felix UNSPECIFIED orcid.org/0000-0001-6177-1755 UNSPECIFIED Fuerweger, Christoph UNSPECIFIED UNSPECIFIED UNSPECIFIED Zwahlen, Daniel R. UNSPECIFIED UNSPECIFIED UNSPECIFIED Foerster, Robert UNSPECIFIED orcid.org/0000-0002-7664-9207 UNSPECIFIED Muacevic, Alexander UNSPECIFIED UNSPECIFIED UNSPECIFIED Windisch, Paul UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-680244
DOI:	10.7759/cureus.22435
Journal or Publication Title:	Cureus J Med Sci
Volume:	14
Number:	2
Date:	2022
Publisher:	CUREUS INC
Place of Publication:	PALO ALTO
ISSN:	2168-8184
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language Medicine, General & Internal Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/68024