Volume 29 Issue 7 | Journal of Biomedical Optics

Journal of Biomedical Optics

VOL. 29 · NO. 7 | July 2024

ISSUES IN PROGRESS

IN PROGRESS

SPIE publishes accepted journal articles as soon as they are approved for publication. Journal issues are considered In Progress until all articles for an issue have been published. Articles published ahead of the completed issue are fully citable.

IN THIS ISSUE

General (1)

Imaging (1)

Microscopy (1)

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General

Depolarization diagrams for circularly polarized light scattering for biological particle monitoring

Nozomi Nishizawa, Asato Esumi, Yukito Ganko

Journal of Biomedical Optics, Vol. 29, Issue 07, 075001, (June 2024) https://doi.org/10.1117/1.JBO.29.7.075001

TOPICS: Depolarization, Particles, Light scattering, Scattered light, Polarization, Scattering, Tissues, Reflection, Mie scattering, Refractive index

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Imaging

Assessing spectral effectiveness in color fundus photography for deep learning classification of retinopathy of prematurity

Behrouz Ebrahimi, David Le, Mansour Abtahi, Albert Dadzie, Alfa Rossi, Mojtaba Rahimi, Taeyoon Son, Susan Ostmo, J. Peter Campbell, R. V. Paul Chan, Xincheng Yao

Journal of Biomedical Optics, Vol. 29, Issue 07, 076001, (June 2024) https://doi.org/10.1117/1.JBO.29.7.076001

TOPICS: Deep learning, Image classification, Color, Photography, Data modeling, Image fusion, Education and training, Performance modeling, Light sources and illumination, Multispectral imaging

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Microscopy

NerveTracker: a Python-based software toolkit for visualizing and tracking groups of nerve fibers in serial block-face microscopy with ultraviolet surface excitation images

Chaitanya Kolluru, Naomi Joseph, James Seckler, Farzad Fereidouni, Richard Levenson, Andrew Shoffstall, Michael Jenkins, David Wilson

Journal of Biomedical Optics, Vol. 29, Issue 07, 076501, (June 2024) https://doi.org/10.1117/1.JBO.29.7.076501

TOPICS: Nerve, Visualization, Image segmentation, Microscopy, Video, 3D applications, Structured optical fibers, Ultraviolet radiation, Imaging systems, Image resolution

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Significance

Information about the spatial organization of fibers within a nerve is crucial to our understanding of nerve anatomy and its response to neuromodulation therapies. A serial block-face microscopy method [three-dimensional microscopy with ultraviolet surface excitation (3D-MUSE)] has been developed to image nerves over extended depths ex vivo. To routinely visualize and track nerve fibers in these datasets, a dedicated and customizable software tool is required.

Aim

Our objective was to develop custom software that includes image processing and visualization methods to perform microscopic tractography along the length of a peripheral nerve sample.

Approach

We modified common computer vision algorithms (optic flow and structure tensor) to track groups of peripheral nerve fibers along the length of the nerve. Interactive streamline visualization and manual editing tools are provided. Optionally, deep learning segmentation of fascicles (fiber bundles) can be applied to constrain the tracts from inadvertently crossing into the epineurium. As an example, we performed tractography on vagus and tibial nerve datasets and assessed accuracy by comparing the resulting nerve tracts with segmentations of fascicles as they split and merge with each other in the nerve sample stack.

Results

We found that a normalized Dice overlap (Dicenorm) metric had a mean value above 0.75 across several millimeters along the nerve. We also found that the tractograms were robust to changes in certain image properties (e.g., downsampling in-plane and out-of-plane), which resulted in only a 2% to 9% change to the mean Dicenorm values. In a vagus nerve sample, tractography allowed us to readily identify that subsets of fibers from four distinct fascicles merge into a single fascicle as we move ∼5 mm along the nerve’s length.

Conclusions

Overall, we demonstrated the feasibility of performing automated microscopic tractography on 3D-MUSE datasets of peripheral nerves. The software should be applicable to other imaging approaches. The code is available at https://github.com/ckolluru/NerveTracker.

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