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Erschienen in:

2019 | OriginalPaper | Buchkapitel

Ensemble of Convolutional Neural Networks for Heart Segmentation

verfasst von : Wilson Fok, Kevin Jamart, Jichao Zhao, Justin Fernandez

Erschienen in: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges

Verlag: Springer International Publishing

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Abstract

Training an ensemble of convolutional neural networks requires much computational resources for a large set of high-resolution medical 3D scans because deep representation requires many parameters and layers. In this study, 100 3D late gadolinium-enhanced (LGE)-MRIs with a spatial resolution of 0.625 mm × 0.625 mm × 0.625 mm from patients with atrial fibrillation were utilized. To contain cost of the training, down-sampling of images, transfer learning and ensemble of network’s past weights were deployed. This paper proposes an image processing stage using down-sampling and contrast limited adaptive histogram equalization, a network training stage using a cyclical learning rate schedule, and a testing stage using an ensemble. While this method achieves reasonable segmentation accuracy with the median of the Dice coefficients at 0.87, this method can be used on a computer with a GPU that has a Kepler architecture and at least 3 GB memory.

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Vorheriges Kapitel V-FCNN: Volumetric Fully Convolution Neural Network for Automatic Atrial Segmentation

Nächstes Kapitel Multi-task Learning for Left Atrial Segmentation on GE-MRI

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Titel: Ensemble of Convolutional Neural Networks for Heart Segmentation
verfasst von: Wilson Fok
Kevin Jamart
Jichao Zhao
Justin Fernandez
Verlag: Springer International Publishing
Buch: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges
Print ISBN: 978-3-030-12028-3

Electronic ISBN: 978-3-030-12029-0

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-12029-0_31

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