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Lumen contour segmentation in ivoct based on n-type cnn

Tang, Junjie; Lan, Yisha; Chen, Sirui; Zhong, Yongshuo; Huang, Chenxi; Peng, Yonghong; Liu, Qinyuan; Cheng, Yongqiang; Chen, Fei; Che, Wenliang


Junjie Tang

Yisha Lan

Sirui Chen

Yongshuo Zhong

Chenxi Huang

Yonghong Peng

Qinyuan Liu

Yongqiang Cheng

Fei Chen

Wenliang Che


Automatic segmentation of lumen contour plays an important role in medical imaging and diagnosis, which is the first step towards the evaluation of morphology of vessels under analysis and the identification of possible atherosclerotic lesions. Meanwhile, quantitative information can only be obtained with segmentation, contributing to the appearance of novel methods which can be successfully applied to intravascular optical coherence tomography (IVOCT) images. This paper proposed a new end-to-end neural network (N-Net) for the automatic lumen segmentation, using multi-scale features based deep neural network, for IVOCT images. The architecture of the N-Net contains a multi-scale input layer, a N-type convolution network layer and a cross-entropy loss function. The multi-scale input layer in the proposed N-Net is designed to avoid the loss of information caused by pooling in traditional U-Net and also enriches the detailed information in each layer. The N-type convolutional network is proposed as the framework in the whole deep architecture. Finally, the loss function guarantees the degree of fidelity between the output of proposed method and the manually labeled output. In order to enlarge the training set, data augmentation is also introduced. We evaluated our method against loss, accuracy, recall, dice similarity coefficient, jaccard similarity coefficient and specificity. The experimental results presented in this paper demonstrate the superior performance of the proposed N-Net architecture, comparing to some existing networks, for enhancing the precision of automatic lumen segmentation and increasing the detailed information of edges of the vascular lumen.


Tang, J., Lan, Y., Chen, S., Zhong, Y., Huang, C., Peng, Y., …Che, W. (2019). Lumen contour segmentation in ivoct based on n-type cnn. IEEE Access, 7, 135573-135581.

Journal Article Type Article
Acceptance Date Sep 12, 2019
Online Publication Date Sep 17, 2019
Publication Date 2019
Deposit Date Jul 9, 2020
Publicly Available Date Jul 15, 2020
Journal IEEE Access
Electronic ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 7
Pages 135573-135581
Keywords IVOCT image; Convolution neural network; Cross entropy loss function; Automatic segmentation
Public URL
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