Tissue injury segmentation of OCT images using deep learning algorithm

Zhaowei Zhong; Wang Liu; Zhifang Li; Dezi Li

doi:10.1117/12.2550109

12 March 2020 Tissue injury segmentation of OCT images using deep learning algorithm

Zhaowei Zhong, Wang Liu, Zhifang Li, Dezi Li

Proceedings Volume 11434, 2019 International Conference on Optical Instruments and Technology: Optical Systems and Modern Optoelectronic Instruments; 114341D (2020) https://doi.org/10.1117/12.2550109
Event: 2019 International Conference on Optical Instruments and Technology, 2019, Beijing, China

Abstract

In recent years, because of its safety and effectiveness, photothermal therapy has also become an important way to treat malignant tumors. However, due to the photothermal conversion effect, the heat deposited in the lesion area will spread to other parts of the biological tissue in the way of heat conduction in the treatment process, which is likely to cause some damage to other normal tissues. Optical coherence tomography (OCT) can reconstruct internal high-resolution images to observe the extent of tissue injury. The image of tissue damage structure obtained by OCT system can observe not only the external damage of tissue, but also the internal damage of tissue. The OCT images of damage tissue are taken as the research object, and a semantic segmentation model of deep learning was constructed to separate and visualize the damage tissue, which can help to accurately identify the damage range quickly.

Citation Download Citation

Zhaowei Zhong, Wang Liu, Zhifang Li, and Dezi Li "Tissue injury segmentation of OCT images using deep learning algorithm", Proc. SPIE 11434, 2019 International Conference on Optical Instruments and Technology: Optical Systems and Modern Optoelectronic Instruments, 114341D (12 March 2020); https://doi.org/10.1117/12.2550109

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KEYWORDS

Tissues

Optical coherence tomography

Injuries

Photothermal effect

Tissue optics

Image segmentation

Safety

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