Mou, L, Zhao, Y, Chen, L et al. (7 more authors) (2019) CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation. In: Lecture Notes in Computer Science. MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, 13-17 Oct 2019, Shenzhen, China. Springer Verlag , pp. 721-730. ISBN 9783030322380
Abstract
The detection of curvilinear structures in medical images, e.g., blood vessels or nerve fibers, is important in aiding management of many diseases. In this work, we propose a general unifying curvilinear structure segmentation network that works on different medical imaging modalities: optical coherence tomography angiography (OCT-A), color fundus image, and corneal confocal microscopy (CCM). Instead of the U-Net based convolutional neural network, we propose a novel network (CS-Net) which includes a self-attention mechanism in the encoder and decoder. Two types of attention modules are utilized - spatial attention and channel attention, to further integrate local features with their global dependencies adaptively. The proposed network has been validated on five datasets: two color fundus datasets, two corneal nerve datasets and one OCT-A dataset. Experimental results show that our method outperforms state-of-the-art methods, for example, sensitivities of corneal nerve fiber segmentation were at least 2% higher than the competitors. As a complementary output, we made manual annotations of two corneal nerve datasets which have been released for public access.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2019. This is an author produced version of a conference paper published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Curvilinear structure; Segmentation; Encoder and decoder |
Dates: |
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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: | 13 May 2020 11:41 |
Last Modified: | 13 May 2020 15:56 |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/978-3-030-32239-7_80 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:160601 |