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Confocal Fluorescence Microscopy

Confocal Fluorescence Microscopy

Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...

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Updated: Aug 28, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

Deep Learning on Basal Cell Carcinoma In Vivo Reflectance Confocal Microscopy Data.

Veronika Shavlokhova¹, Michael Vollmer¹, Patrick Gholam²

¹Department of Oral and Maxillofacial Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany.

Journal of Personalized Medicine

|September 23, 2022

Summary

A deep learning algorithm shows potential for detecting basal cell carcinoma (BCC) during surgery using in vivo confocal microscopy. Further studies are needed to improve accuracy in identifying these common head and neck skin cancers.

Keywords:

BCC artificial intelligence deep learning reflectance confocal laser microscopy

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Author Spotlight: Developing a Tool for Using Inverted Confocal Microscopes for In Vivo Intravital Imaging

Published on: June 30, 2023

Area of Science:

Dermatology
Oncology
Medical Imaging

Background:

Head and neck skin cancers, particularly basal cell carcinoma (BCC), present significant diagnostic and therapeutic challenges.
Radical resection can cause functional and aesthetic impairment; precise margin assessment is crucial to balance invasiveness and cancer removal.
Intraoperative margin assessment using reflectance confocal laser microscopy (RCM) offers high-resolution cellular visualization.

Purpose of the Study:

To investigate the feasibility of applying a deep learning algorithm for automated detection of malignant areas in in vivo RCM images.
To assess the potential of combining RCM with deep learning for objective, investigator-independent interpretation of skin cancer margins.

Main Methods:

A preliminary feasibility study involving 62 patients with histologically confirmed BCC in the head and neck region.
In vivo confocal laser microscope scanning was performed on all patients.
Approximately 382 images of BCC structures were acquired, annotated, and used for deep learning model training.

Main Results:

A convolutional neural network model (MobileNet) was employed for automated detection.
The model achieved a sensitivity of 46% and a specificity of 85% in identifying BCC regions.

Conclusions:

Preliminary results indicate the potential of automated BCC detection using in vivo RCM.
The study highlights both the capabilities and limitations of the current deep learning approach.
Larger-scale studies are necessary to enhance the predictability and clinical utility of this technology.