Machine learning approach to synthesizing multiphoton microscopic images from reflectance confocal (Conference Presentation)

Mugdha, Arya Chowdhury; Wilson, Jesse W.

doi:10.1117/12.2510473

Cited by 1 publication

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“…Multiphoton microscopy can also illuminate other endogenous structures, providing more detailed information regarding the organization of the collagen fibrils. Recent studies have also used deep learning to infer fluorescence and nonlinear contrast from the texture and morphology of RCM images by using multiphoton microscopy images as ground truth 51,52 .…”

Section: Discussionmentioning

confidence: 99%

Biopsy-free in vivo virtual histology of skin using deep learning

Garfinkel²,

Zhang

et al. 2021

Light Sci Appl

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An invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin tumors. The process is cumbersome and time-consuming, often leading to unnecessary biopsies and scars. Emerging noninvasive optical technologies such as reflectance confocal microscopy (RCM) can provide label-free, cellular-level resolution, in vivo images of skin without performing a biopsy. Although RCM is a useful diagnostic tool, it requires specialized training because the acquired images are grayscale, lack nuclear features, and are difficult to correlate with tissue pathology. Here, we present a deep learning-based framework that uses a convolutional neural network to rapidly transform in vivo RCM images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution, enabling visualization of the epidermis, dermal-epidermal junction, and superficial dermis layers. The network was trained under an adversarial learning scheme, which takes ex vivo RCM images of excised unstained/label-free tissue as inputs and uses the microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground truth. We show that this trained neural network can be used to rapidly perform virtual histology of in vivo, label-free RCM images of normal skin structure, basal cell carcinoma, and melanocytic nevi with pigmented melanocytes, demonstrating similar histological features to traditional histology from the same excised tissue. This application of deep learning-based virtual staining to noninvasive imaging technologies may permit more rapid diagnoses of malignant skin neoplasms and reduce invasive skin biopsies.

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Section: Discussionmentioning

confidence: 99%