Folle, L, Vesal, S, Ravikumar, N et al. (1 more author) (2019) Dilated Deeply Supervised Networks for Hippocampus Segmentation in MRI. In: Handels, H, Deserno, TM, Maier, A, Maier-Hein, KH, Palm, C and Tolxdorff, T, (eds.) Informatik aktuell. BVM 2019: Bildverarbeitung für die Medizin, 17-19 Mar 2019, Lübeck, Germany. Springer Vieweg , pp. 68-73. ISBN 978-3-658-25325-7
Abstract
Tissue loss in the hippocampi has been heavily correlated with the progression of Alzheimer’s Disease (AD). The shape and structure of the hippocampus are important factors in terms of early AD diagnosis and prognosis by clinicians. However, manual segmentation of such subcortical structures in MR studies is a challenging and subjective task. In this paper, we investigate variants of the well known 3D U-Net, a type of convolution neural network (CNN) for semantic segmentation tasks.We propose an alternative form of the 3D U-Net, which uses dilated convolutions and deep supervision to incorporate multi-scale information into the model. The proposed method is evaluated on the task of hippocampus head and body segmentation in an MRI dataset, provided as part of the MICCAI 2018 segmentation decathlon challenge. The experimental results show that our approach outperforms other conventional methods in terms of different segmentation accuracy metrics.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Editors: |
|
Copyright, Publisher and Additional Information: | © 2019, Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature. This is a post-peer-review, pre-copyedit version of an article published in Informatik aktuell. The final authenticated version is available online at: https://doi.org/10.1007/978-3-658-25326-4_18. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Aug 2019 10:48 |
Last Modified: | 07 Feb 2020 01:39 |
Status: | Published |
Publisher: | Springer Vieweg |
Identification Number: | 10.1007/978-3-658-25326-4_18 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:149274 |