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Kepp T, Droigk C, Casper M, et al. Segmentation of mouse skin layers in optical coherence tomography image data using deep convolutional neural networks. Biomed Opt Express. 2019;10(7):3484-3496doi: 10.1364/BOE.10.003484.
Kepp, T., Droigk, C., Casper, M., Evers, M., Hüttmann, G., Salma, N., Manstein, D., Heinrich, M. P., & Handels, H. (2019). Segmentation of mouse skin layers in optical coherence tomography image data using deep convolutional neural networks. Biomedical optics express, 10(7), 3484-3496. https://doi.org/10.1364/BOE.10.003484
Kepp, Timo, et al. "Segmentation of mouse skin layers in optical coherence tomography image data using deep convolutional neural networks." Biomedical optics express vol. 10,7 (2019): 3484-3496. doi: https://doi.org/10.1364/BOE.10.003484
Kepp T, Droigk C, Casper M, Evers M, Hüttmann G, Salma N, Manstein D, Heinrich MP, Handels H. Segmentation of mouse skin layers in optical coherence tomography image data using deep convolutional neural networks. Biomed Opt Express. 2019 Jun 21;10(7):3484-3496. doi: 10.1364/BOE.10.003484. eCollection 2019 Jul 01. PMID: 31467791; PMCID: PMC6706029.
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Biomed Opt Express. 2019 Jun 21;10(7):3484-3496. doi: 10.1364/BOE.10.003484. eCollection 2019 Jul 01.

Segmentation of mouse skin layers in optical coherence tomography image data using deep convolutional neural networks.

Biomedical optics express

Timo Kepp, Christine Droigk, Malte Casper, Michael Evers, Gereon Hüttmann, Nunciada Salma, Dieter Manstein, Mattias P Heinrich, Heinz Handels

Affiliations

  1. Institute of Medical Informatics, University of Lübeck, Lübeck, Germany.
  2. Graduate School for Computing in Medicine and Life Sciences, University of Lübeck, Lübeck, Germany.
  3. Institute for Signal Processing, University of Lübeck, Lübeck, Germany.
  4. Institute of Biomedical Optics, University of Lübeck, Lübeck, Germany.
  5. Cutaneous Biology Research Center, Massachusetts General Hospital, Boston, USA.

PMID: 31467791 PMCID: PMC6706029 DOI: 10.1364/BOE.10.003484

Abstract

Optical coherence tomography (OCT) enables the non-invasive acquisition of high-resolution three-dimensional cross-sectional images at micrometer scale and is mainly used in the field of ophthalmology for diagnosis as well as monitoring of eye diseases. Also in other areas, such as dermatology, OCT is already well established. Due to its non-invasive nature, OCT is also employed for research studies involving animal models. Manual evaluation of OCT images of animal models is a challenging task due to the lack of imaging standards and the varying anatomy among models. In this paper, we present a deep learning algorithm for the automatic segmentation of several layers of mouse skin in OCT image data using a deep convolutional neural network (CNN). The architecture of our CNN is based on the U-net and is modified by densely connected convolutions. We compared our adapted CNN with our previous algorithm, a combination of a random forest classification and a graph-based refinement, and a baseline U-net. The results showed that, on average, our proposed CNN outperformed our previous algorithm and the baseline U-net. In addition, a reduction of outliers could be observed through the use of densely connected convolutions.

Conflict of interest statement

The authors declare that there are no conflicts of interest related to this article.

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