Welcome to Scholar Publishing Group

International Journal of Engineering Technology and Construction, 2021, 2(2); doi: 10.38007/IJETC.2021.020202.

OCT in Fundus Examination of Mice and Research on Automatic Segmentation and Measurement of OCT Images

Author(s)

Ping Jiang

Corresponding Author:
Ping Jiang
Affiliation(s)

Changchun University of Chinese Medicine, Jilin, China

Abstract

The reason why most ophthalmic medical research was carried out in mice is that the genes of mice and human are extremely similar. The retinal structure of a mature mouse has an important reference function for animals with ophthalmic diseases. Optical coherence tomography (OCT) is used to detect the delay of light echo time and obtain the layers of the detected tissue cells. The images of different layers of retina provided by OCT are closely related to histomorphology. The purpose of this paper is to study the application of OCT in fundus examination of mice and automatic segmentation and measurement of OCT images. First of all, due to the great difference in refractive structure between human and mouse eyes, this paper established a mouse retina OCT imaging device to study the image presentation effect of different sampling methods, so as to achieve the accurate display of mouse eye image; secondly, using the automatic segmentation and measurement technology of OCT image, the edge is detected step by step and the effect of image presentation is enhanced, and the contour is successfully extracted and compared with manual segmentation Finally, we compared the data obtained from mouse retinal tissue section and the data detected by OCT image. The final results show that the data detected by OCT imaging equipment can completely replace the data obtained by mouse tissue section, and even the improved OCT imaging device can detect the data that cannot be obtained by tissue section. This method not only improves the time-consuming and laborious disadvantages of manual mouse tissue sectioning, but also effectively promotes the establishment of ophthalmic disease model through the high-quality images obtained by the computer automatic processing of OCT imaging device.

Keywords

Retinal Structure, Ophthalmic Diseases, Optical Coherence Tomography, Computer Automation

Cite This Paper

Ping Jiang. OCT in Fundus Examination of Mice and Research on Automatic Segmentation and Measurement of OCT Images. International Journal of Engineering Technology and Construction (2021), Vol. 2, Issue 2: 14-26. https://doi.org/10.38007/IJETC.2021.020202.

References

[1] Kuang, T. M. , Tsai, S. Y. , Liu, J. L. , Ko, Y. C. , Lee, S. M. , & Chou, P. . (2017). Changes in refractive status in an elderly chinese population in a 7-year follow-up: the shihpai eye study. Journal of the Chinese Medical Association, 80(10), 673-678.

[2] Kugler, M. , Schlecht, A. , Fuchshofer, R. , Schmitt, S. I. , Kleiter, I. , & Aigner, L. , et al. (2017). Smad7 deficiency stimulates müller progenitor cell proliferation during the development of the mammalian retina. Histochemistry & Cell Biology, 148(1), 1-12.

[3] Fang, L. , Cunefare, D. , Wang, C. , Guymer, R. H. , & Farsiu, S. . (2017). Automatic segmentation of nine retinal layer boundaries in oct images of non-exudative amd patients using deep learning and graph search. Biomedical Optics Express, 8(5), 2732-2744. DOI: 10.1364/BOE.8.002732

[4] Espinosa, L. , Arciniegas, A. , Cortes, Y. , Prieto, F. , & Brancheriau, L. . (2017). Automatic segmentation of acoustic tomography images for the measurement of wood decay. Wood ence and Technology, 51(1), 69-84. DOI: 10.1007/s00226-016-0878-1

[5] Tang, S. N. , Hsiang, C. Y. , Huang, S. J. , & Chen, W. W. . (2018). Fdoct imaging processor for portable oct systems with high imaging rate. ICE Electronics Express, 15(3), 20171128-20171128.

[6] Hyeon Cheol Park, Jessica MavadiaShukla, Wu Yuan, Milad Alemohammad, & Xingde Li. (2017). Broadband rotary joint for high-speed ultrahigh-resolution endoscopic oct imaging at 800nm. Optics Letters, 42(23), 4978. DOI: 10.1364/OL.42.004978

[7] Raza, A. , Li, F. , Xu, X. , & Tang, J. . (2017). Optimization of ultrasonic-assisted extraction of antioxidant polysaccharides from the stem of trapa quadrispinosa using response surface methodology. International journal of biological macromolecules, 94(Pt A), 335-344.

[8] De Luca, G. M. R. , Desclos, E. , Breedijk, R. M. P. , Dolz-Edo, L. , Smits, G. J. , & Nahidiazar, L. , et al. (2017). Configurations of the re‐scan confocal microscope (rcm) for biomedical applications. Journal of Microscopy, 266(2), 166-177. DOI: 10.1111/jmi.12526

[9] Tanaka, M. , & Shiina, T. . (2018). High-precision absolute value measurement of reflection by time domain oct. IEEJ Transactions on Fundamentals and Materials, 138(5), 198-203.

[10] Yang, F. , & Yang, J. . (2019). Mean-square performance of the modified frequency-domain block lms algorithm. Signal Processing, 163(OCT.), 18-25.

[11] Castillo, J. , Mocquet, A. , & Saracco, G. . (2018). Wavelet transform: a tool for the interpretation of upper mantle converted phases at high frequency. Geophysical Research Letters, 28(22), 4327-4330.

[12] Pal, C. , Das, P. , Chakrabarti, A. , & Ghosh, R. . (2017). Rician noise removal in magnitude mri images using efficient anisotropic diffusion filtering. International Journal of Imaging Systems & Technology, 27(3), 248-264.

[13] Liu, P. , Ha, R. , & Jia, K. . (2017). Improved adaptive median filter and its' application. Journal of Bjing University of Technology, 43(4), 581-586.

[14] Setiadi, D. R. I. M. , & Jumanto, J. . (2018). An enhanced lsb-image steganography using the hybrid canny-sobel edge detection. Cybernetics & Information Technologies, 18(2), 74-88.

[15] Sui, X. , Zheng, Y. , Wei, B. , Bi, H. , Wu, J. , & Pan, X. , et al. (2017). Choroid segmentation from opticalcoherence tomography with graph-edge weights learned from deep convolutional neural networks. Neurocomputing, 237(MAY10), 332-341