Rampun, A., Jarvis, D., Griffiths, P.D. orcid.org/0000-0002-2706-5897 et al. (3 more authors) (2021) Single-input multi-output U-Net for automated 2D foetal brain segmentation of MR images. Journal of Imaging, 7 (10). 200. ISSN 2313-433X
Abstract
In this work, we develop the Single-Input Multi-Output U-Net (SIMOU-Net), a hybrid network for foetal brain segmentation inspired by the original U-Net fused with the holistically nested edge detection (HED) network. The SIMOU-Net is similar to the original U-Net but it has a deeper architecture and takes account of the features extracted from each side output. It acts similar to an ensemble neural network, however, instead of averaging the outputs from several independently trained models, which is computationally expensive, our approach combines outputs from a single network to reduce the variance of predications and generalization errors. Experimental results using 200 normal foetal brains consisting of over 11,500 2D images produced Dice and Jaccard coefficients of 94.2 ± 5.9% and 88.7 ± 6.9%, respectively. We further tested the proposed network on 54 abnormal cases (over 3500 images) and achieved Dice and Jaccard coefficients of 91.2 ± 6.8% and 85.7 ± 6.6%, respectively.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | foetal brain segmentation; MRI; U-Net; HED network; deep learning; convolutional neural network |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Infection and Immunity (Sheffield) |
Funding Information: | Funder Grant number NIHR Evaluation Trials and Studies Coordinating Centre NIHRDH-HTA/09/06/01 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Oct 2021 13:20 |
Last Modified: | 08 Oct 2021 13:20 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/jimaging7100200 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:178848 |