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

Abstract

Optical coherence tomography (OCT) enables the non-invasive acquisition of highresolution three-dimensional cross-sectional images at a 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.

Phase unwrapping based on a residual en-decoder network for phase images in Fourier domain Doppler optical coherence tomography

“…The residual thread can compute the residuals at full image resolution with FRRB and RB that make the high-level features to go through the network [38][39][40]. Meanwhile, it has been proven that it is much easier to optimize a residual mapping than the original plain network [41]. The full pre-activation RB was adopted in our network architecture, as shown in Fig.…”

Segmentation of retinal low-cost optical coherence tomography images using deep learning

“…Furthermore, the RETOUCH challenge was recently organized to measure the performance of state-of-the-art methods for the detection and segmentation of retinal fluids in OCT. 10 In this work, for the first time, the total retina volume as well as PED is segmented in SELF-OCT image data using a deep learning-based approach. The segmentation approach builds on our preliminary work 11,12 and consists of a CNN that segments the total retina as well as PEDs in three-dimensional scans of the SELF-OCT and is based on the popular U-Net architecture. 13 In comparison to commercially available clinical OCT systems, the scans of the SELF-OCT show a lower quality with a reduced signal-to-noise ratio (SNR).…”

Automatic Segmentation of Epidermis and Hair Follicles in Optical Coherence Tomography Images of Normal Skin by Convolutional Neural Networks

“…As well as other quantitative analyses of skin OCTs, the calculation of the epidermal layer thickness, which is important for the diagnosis of several skin disorders, often requires manual segmentation that is very time-consuming and suffers from inter and intra-observer variability. This fact has motivated the development of semi-and fully automated epidermis segmentation methods from OCT images (5)(6)(7)(8)(9). Li et al defined the segmentation of the epidermis in three steps: preprocessing weighted by least squares, detection of the surface of the skin based on graphics, and local integral projection for the detection of the DEJ.…”

“…Note that the aforementioned works were based on classical image processing techniques. In fact, despite the existence of state-ofthe-art methods for segmentation of different skin structures, only few are based on deep learning algorithms (8,9). Calderon-Delgado et al proposed a fully convolutional network (FCN) to segment 1756 human skin OCT images into dermis, dermalepidermal junction, epidermis, glycerol, and glass, obtaining an average accuracy of 88, 91, 90, and 96%, respectively.…”

“…They divided the skin into five classes: (epi-)dermis layer, subcutaneous fat layer, fascia and muscle layer, tattoos and background. They applied their algorithms to 72 B-scans of the mice inguinal region and achieved an average Dice similarity coefficient of 0.86 over all segmented skin structures (9). Note that this work did not differentiate between the epidermis and the dermis region.…”