Automatic segmentation of the thalamus using a massively trained 3D convolutional neural network: higher sensitivity for the detection of reduced thalamus volume by improved inter-scanner stability

Objectives To develop an automatic method for accurate and robust thalamus segmentation in T1w-MRI for widespread clinical use without the need for strict harmonization of acquisition protocols and/or scanner-specific normal databases. Methods A three-dimensional convolutional neural network (3D-CNN...

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Bibliographic Details
Published in:	European radiology Vol. 33; no. 3; pp. 1852 - 1861
Main Authors:	Opfer, Roland, Krüger, Julia, Spies, Lothar, Ostwaldt, Ann-Christin, Kitzler, Hagen H., Schippling, Sven, Buchert, Ralph
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2023 Springer Nature B.V
Subjects:	Artificial neural networks Atrophy Diagnostic Radiology Estimates Humans Image processing Image Processing, Computer-Assisted - methods Image segmentation Imaging Imaging Informatics and Artificial Intelligence Internal Medicine Interventional Radiology Magnetic resonance imaging Magnetic Resonance Imaging - methods Masks Medicine Medicine & Public Health Multiple sclerosis Multiple Sclerosis - diagnostic imaging Neural networks Neural Networks, Computer Neuroradiology Radiology Scanners Sensitivity Stability analysis Thalamus Thalamus - diagnostic imaging Ultrasound Thalamus Multiple sclerosis Magnetic resonance imaging Neural networks
ISSN:	1432-1084, 0938-7994, 1432-1084
Online Access:	Get full text
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