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Image segmentation of mouse eye in vivo with optical coherence tomography based on Bayesian classification

  • Fei Ma ORCID logo EMAIL logo , Shengbo Wang , Yanfei Guo , Cuixia Dai and Jing Meng EMAIL logo
Published/Copyright: January 5, 2024
Biomedical Engineering / Biomedizinische Technik
From the journal Biomedical Engineering / Biomedizinische Technik Volume 69 Issue 3

Abstract

Objectives

Optical coherence tomography (OCT) is a new imaging technology that uses an optical analog of ultrasound imaging for biological tissues. Image segmentation plays an important role in dealing with quantitative analysis of medical images.

Methods

We have proposed a novel framework to deal with the low intensity problem, based on the labeled patches and Bayesian classification (LPBC) model. The proposed method includes training and testing phases. During the training phase, firstly, we manually select the sub-images of background and Region of Interest (ROI) from the training image, and then extract features by patches. Finally, we train the Bayesian model with the features. The segmentation threshold of each patch is computed by the learned Bayesian model.

Results

In addition, we have collected a new dataset of mouse eyes in vivo with OCT, named MEVOCT, which can be found at URL https://17861318579.github.io/LPBC. MEVOCT consists of 20 high-resolution images. The resolution of every image is 2048 × 2048 pixels.

Conclusions

The experimental results demonstrate the effectiveness of the LPBC method on the new MEVOCT dataset. The ROI segmentation is of great importance for the distortion correction.

Keywords: mouse eye in vivo ; optical coherence tomography; image segmentation; labeled patches
Corresponding authors: Fei Ma and Jing Meng, School of Computer Science, Qufu Normal University, Yantai Road, Rizhao, 276827, Shandong, China, E-mail: (F. Ma), (J. Meng)

  1. Research ethics: The Bioethics Committee of the Centro de Investigacion Cientifica y de Educacion Superior de Ensenada (Ensenada, Baja California, Mexico) approved this study (No. HUM 2020 03). All procedures performed were approved for studies involving human participants according to the ethical standards of the institutional and/or national research committee and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

  2. Informed consent: Informed consent was obtained from all individual participants included in the study.

  3. Author contributions: Fei Ma and Jing Meng contributed to the conception of the study; Shengbo Wang performed the experiment and con tributed significantly to analysis and manuscript preparation; Fei Ma wrote the manuscript; Cuixia Dai helped perform the analysis with constructive discussions.

  4. Competing interests: The authors declare that they have no conflict of interest.

  5. Research funding: Partial financial support was received from the Natural Science Foundation of Shandong Province (No: ZR2020MF105) and Qufu Normal University Doctor Fund (No: 20190080).

  6. Data availability: MEVOCT dataset and MakeGT for Ground truth can be found at URL: https://17861318579.github.io/LPBC.

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Received: 2023-06-16
Accepted: 2023-12-22
Published Online: 2024-01-05
Published in Print: 2024-06-25

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/bmt-2023-0266
Keywords for this article
mouse eye in vivo; optical coherence tomography; image segmentation; labeled patches

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. Computational models of bone fracture healing and applications: a review
  4. Research Articles
  5. Assessing standing balance with MOTI: a validation study
  6. Effect of non-thermal plasma treatment and resin cements on the bond strength of zirconia ceramics with different yttria concentrations
  7. A new approach towards extracorporeal gas exchange and first in vitro results
  8. Estimation of heart rate and respiratory rate by monitoring cardiopulmonary signals with flexible sensor
  9. Hand gesture recognition with deep residual network using Semg signal
  10. Crowdsourcing image segmentation for deep learning: integrated platform for citizen science, paid microtask, and gamification
  11. Image segmentation of mouse eye in vivo with optical coherence tomography based on Bayesian classification
  12. Impact of the new European medical device regulation: a two-year comparison
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