Paper Info
Title
CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation.
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.
Year
DOI
Venue
2019
10.1007/978-3-030-32239-7_80
Lecture Notes in Computer Science
Keywords
DocType
Volume
Curvilinear structure,Segmentation,Encoder and decoder
Conference
11764
ISSN
Citations 
PageRank 
0302-9743
4
0.41
References 
Authors
0
10
Name
Order
Citations
PageRank
Lei Mou1162.64
Yitian Zhao224633.15
Li Chen340.41
Jun Cheng421420.65
Zaiwang Gu5855.88
Huaying Hao6694.82
Hong Qi7101.86
Yalin Zheng826434.69
Alejandro F. Frangi940.41
Jiang Liu1033534.30