Semisupervised representative learning for measuring epidermal thickness in human subjects in optical coherence tomography by leveraging datasets from rodent models

Yubo Ji, Shufan Yang, Kanheng Zhou, Jie Lu, Ruikang Wang, Holly R Rocliffe, Antonella Pellicoro, Jenna L. Cash, Chunhui Li (Lead / Corresponding author), Zhihong Huang

Research output: Contribution to journalArticlepeer-review

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Abstract

Significance: Morphological changes in the epidermis layer are critical for the diagnosis and assessment of various skin diseases. Due to its noninvasiveness, optical coherence tomography (OCT) is a good candidate for observing microstructural changes in skin. Convolutional neural network (CNN) has been successfully used for automated segmentation of the skin layers of OCT images to provide an objective evaluation of skin disorders. Such method is reliable, provided that a large amount of labeled data is available, which is very time-consuming and tedious. The scarcity of patient data also puts another layer of difficulty to make the model more generalizable.

Aim: We developed a semisupervised representation learning method to provide data augmentations.

Approach: We used rodent models to train neural networks for accurate segmentation of clinical data.

Result: The learning quality is maintained with only one OCT labeled image per volume that is acquired from patients. Data augmentation introduces a semantically meaningful variance, allowing for better generalization. Our experiments demonstrate the proposed method can achieve accurate segmentation and thickness measurement of the epidermis.

Conclusion: This is the first report of semisupervised representative learning applied to OCT images from clinical data by making full use of the data acquired from rodent models. The proposed method promises to aid in the clinical assessment and treatment planning of skin diseases.

Original languageEnglish
Article number085002
Number of pages14
JournalJournal of Biomedical Optics
Volume27
Issue number8
DOIs
Publication statusPublished - 19 Aug 2022

Keywords

  • Algorithms
  • Animals
  • Epidermis/diagnostic imaging
  • Humans
  • Research Subjects
  • Rodentia
  • Tomography, Optical Coherence/methods
  • acute burning wound
  • scab
  • semisupervised learning
  • optical coherence tomography
  • re-epithelialization
  • epidermis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomedical Engineering
  • Biomaterials

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