Related Experiment Video For Autofluorescence
Updated: Jan 27, 2026
Author Spotlight: Anterior HR-OCT as a Non-Invasive Tool for Characterizing Ocular Surface Squamous Neoplasia
Published on: August 9, 2024
Novel automated non invasive detection of ocular surface squamous neoplasia using multispectral autofluorescence
Abbas Habibalahi1, Chandra Bala2, Alexandra Allende3
1ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, North Ryde, NSW, 2109, Australia; School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, 2109, Australia; Graduate School of Biomedical Engineering, University of New South Wales, Sydney, 2032, NSW, Australia.
Purpose:
Diagnosing Ocular surface squamous neoplasia (OSSN) using newly designed multispectral imaging technique.
Methods:
Eighteen patients with histopathological diagnosis of Ocular Surface Squamous Neoplasia (OSSN) were recruited. Their previously collected biopsy specimens of OSSN were reprocessed without staining to obtain auto fluorescence multispectral microscopy images. This technique involved a custom-built spectral imaging system with 38 spectral channels. Inter and intra-patient frameworks were deployed to automatically detect and delineate OSSN using machine learning methods. Different machine learning methods were evaluated, with K nearest neighbor and Support Vector Machine chosen as preferred classifiers for intra- and inter-patient frameworks, respectively. The performance of the technique was evaluated against a pathological assessment.
Results:
Quantitative analysis of the spectral images provided a strong multispectral signature of a relative difference between neoplastic and normal tissue both within each patient (at p < 0.0005) and between patients (at p < 0.001). Our fully automated diagnostic method based on machine learning produces maps of the relatively well circumscribed neoplastic-non neoplastic interface. Such maps can be rapidly generated in quasi-real time and used for intraoperative assessment. Generally, OSSN could be detected using multispectral analysis in all patients investigated here. The cancer margins detected by multispectral analysis were in close and reasonable agreement with the margins observed in the H&E sections in intra- and inter-patient classification, respectively.
Conclusions:
This study shows the feasibility of using multispectral auto-fluorescence imaging to detect and find the boundary of human OSSN. Fully automated analysis of multispectral images based on machine learning methods provides a promising diagnostic tool for OSSN which can be translated to future clinical applications.
Related Concept Videos
01:16Surface Tension and Surface Energy
Consider a beaker filled with liquid. The bulk molecules in the liquid experience equal attractive forces on all sides with the surrounding molecules. However, the surface molecules experience a net attractive force downward due to the bulk molecules. The surface of the liquid behaves like a stretched membrane,...
01:14Wood Surfacing
The equipment used in the surfacing process is a plane equipped with rotating blades. This tool efficiently smoothens the wood surface and can...
01:21Cell-surface Signaling
01:25Distribution Reliability and Automation
01:23Surface Appendages of Archaea
01:18Surface Membrane Barriers
The outer layer of the skin, the epidermis, is a robust barrier comprising layers of closely packed keratinized cells. This dense arrangement prevents microbes from penetrating the body. The periodic shedding of epidermal cells...