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Proceedings Paper

A tissue classification method of IVOCT images using rectangle region cropped along the circumferential direction based on deep learning
Author(s): Xinbo Ren; Haiyuan Wu; Qian Chen; Takashi Kubo; Takashi Akasaka
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Paper Abstract

Coronary artery disease (CAD) as a common disease is now indeed affecting the quality of daily life of patients. Qualification analysis of the causing reasons for this kind of disease needs more vessel inner tissue (healthy or not healthy) information in detail. Recent years, an intravascular OCT technology is starting implemented to the patients for a appropriate treatment. Lesion tissue analysis of thousands of IVOCT image data per patient is time-consuming and lower efficiency especially on manual analyzing. Traditional machine learning methods are always applied to investigate the feature extracted from the image data with some special feature engineering technologies, but for deeper abstract features, it's still difficult to draw out. Currently, the utility of deep learning method to image target detection and classification tasks has won a great success and it's generally common to use the deep learning method attack many computer version issues. In this paper, we propose a method based on the Convolutional Neural Network (CNN) to model a VGG-Net-like for category classification of vessel lesion tissues. We preprocess the IVOCT image with catheter and guide-wire removal methods and obtain the lumen boundary. Analyzing the intensity of vessel tissues with light attenuation, we crop rectangle regions with fixed size along the circumferential direction to gain a number of patches as the input samples of CNN. Three kinds of input type, LBP-based single channel, RGB channels and merging-channel containing LBP and RGB, are fed into the model we built to discuss the prediction results.

Paper Details

Date Published: 27 March 2019
PDF: 7 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 1105015 (27 March 2019); doi: 10.1117/12.2520688
Show Author Affiliations
Xinbo Ren, Wakayama Univ. (Japan)
Shangluo Univ. (China)
Haiyuan Wu, Wakayama Univ. (Japan)
Qian Chen, Wakayama Univ. (Japan)
Takashi Kubo, Wakayama Medical Ctr. (Japan)
Takashi Akasaka, Wakayama Medical Ctr. (Japan)


Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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