Visualization and Validation of The Microstructures in The Airway Wall in vivo Using Diffractive Optical Coherence Tomography

Quantification of smooth muscle in human airways by polarization-sensitive optical coherence tomography requires correction for perichondrium

Quantifying airway smooth muscle (ASM) in patients with asthma raises the possibility of improved and personalized disease management. Endobronchial polarization-sensitive optical coherence tomography (PS-OCT) is a promising quantitative imaging approach that is in the early stages of clinical translation. To date, only animal tissues have been used to assess the accuracy of PS-OCT to quantify absolute (rather than relative) ASM in cross sections with directly matched histological cross sections as validation. We report the use of whole fresh human and pig airways to perform a detailed side-by-side qualitative and quantitative validation of PS-OCT against gold-standard histology. We matched and quantified 120 sections from five human and seven pig (small and large) airways and linked PS-OCT signatures of ASM to the tissue structural appearance in histology. Notably, we found that human cartilage perichondrium can share with ASM the properties of birefringence and circumferential alignment of fibers, making it a significant confounder for ASM detection. Measurements not corrected for perichondrium overestimated ASM content several-fold (P < 0.001, paired t test). After careful exclusion of perichondrium, we found a strong positive correlation (r = 0.96, P < 0.00001) of ASM area measured by PS-OCT and histology, supporting the method's application in human subjects. Matching human histology further indicated that PS-OCT allows conclusions on the intralayer composition and in turn potential contractile capacity of ASM bands. Together these results form a reliable basis for future clinical studies.NEW & NOTEWORTHY Polarization-sensitive optical coherence tomography (PS-OCT) may facilitate in vivo measurement of airway smooth muscle (ASM). We present a quantitative validation correlating absolute ASM area from PS-OCT to directly matched histological cross sections using human tissue. A major confounder for ASM quantification was observed and resolved: fibrous perichondrium surrounding hyaline cartilage in human airways presents a PS-OCT signature similar to ASM for birefringence and optic axis orientation. Findings impact the development of automated methods for ASM segmentation.

Unraveling the complexity of Optical Coherence Tomography image segmentation using machine and deep learning techniques: A review

Optical Coherence Tomography (OCT) is an emerging technology that provides three-dimensional images of the microanatomy of biological tissue in-vivo and at micrometer-scale resolution. OCT imaging has been widely used to diagnose and manage various medical diseases, such as macular degeneration, glaucoma, and coronary artery disease. Despite its wide range of applications, the segmentation of OCT images remains difficult due to the complexity of tissue structures and the presence of artifacts. In recent years, different approaches have been used for OCT image segmentation, such as intensity-based, region-based, and deep learning-based methods. This paper reviews the major advances in state-of-the-art OCT image segmentation techniques. It provides an overview of the advantages and limitations of each method and presents the most relevant research works related to OCT image segmentation. It also provides an overview of existing datasets and discusses potential clinical applications. Additionally, this review gives an in-depth analysis of machine learning and deep learning approaches for OCT image segmentation. It outlines challenges and opportunities for further research in this field.

Direct Visualization and Quantitative Imaging of Small Airway Anatomy In Vivo Using Deep Learning Assisted Diffractive OCT

OBJECTIVE/BACKGROUND

In vivo imaging and quantification of the microstructures of small airways in three dimensions (3D) allows a better understanding and management of airway diseases, such as asthma and chronic obstructive pulmonary disease (COPD). At present, the resolution and contrast of the currently available conventional optical coherence tomography (OCT) imaging technologies operating at 1300 nm remain challenging to directly visualize the fine microstructures of small airways in vivo.

METHODS

We developed an ultrahigh-resolution diffractive endoscopic OCT at 800 nm to afford a resolving power of 1.7 µm (in tissue) with an improved contrast and a custom deep residual learning based image segmentation framework to perform accurate and automated 3D quantification of airway anatomy.

RESULTS

The 800-nm diffractive OCT enabled the direct delineation of the structural components in the small airway wall in vivo. We further first demonstrated the 3D anatomic quantification of critical tissue compartments of small airways in sheep using the automated segmentation method.

CONCLUSION

The deep learning assisted diffractive OCT provides a unique ability to access the small airways, directly visualize and quantify the important tissue compartments, such as airway smooth muscle, in the airway wall in vivo in 3D.

SIGNIFICANCE

These pilot results suggest a potential technology for calculating volumetric measurements of small airways in patients in vivo.