Volume 10 Issue 3 | Neurophotonics

Neurophotonics

VOL. 10 · NO. 3 | July 2023

CONTENTS

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Editorial (1)

Research Papers (10)

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Editorial

Warriors of Science: Ukraine’s Academic Endeavors

Daria Bogatova

Neurophotonics, Vol. 10, Issue 03, 030101, (August 2023) https://doi.org/10.1117/1.NPh.10.3.030101

TOPICS: Neurophotonics, Medicine, Neuroscience, Chaos, Polarized light, Medical research, Lung cancer, Laser optics, Laboratories, Heart

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Research Papers

Differential reductions in the capillary red-blood-cell flux between retina and brain under chronic global hypoperfusion

Baoqiang Li, Ji Leng, Ikbal Şencan-Eğilmez, Hajime Takase, Mohammed Ali H. Alfadhel, Buyin Fu, Mahnaz Shahidi, Eng Lo, Ken Arai, Sava Sakadžić

Neurophotonics, Vol. 10, Issue 03, 035001, (June 2023) https://doi.org/10.1117/1.NPh.10.3.035001

TOPICS: Capillaries, Retina, Red blood cells, Brain, Animals, Animal model studies, Brain diseases, Blood circulation, Biological imaging, Retinal diseases

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Millimetric scale two-photon Bessel-Gauss beam light sheet microscopy with three-axis isotropic resolution using an axicon lens

Cléophace Akitegetse, Thomas Charland, Mireille Quémener, Charles Gora, Véronique Rioux, Michel Piché, Yves De Koninck, Martin Lévesque, Daniel C. Côté

Neurophotonics, Vol. 10, Issue 03, 035002, (June 2023) https://doi.org/10.1117/1.NPh.10.3.035002

TOPICS: Axicons, Microscopes, Objectives, Brain, Light sources and illumination, Gaussian beams, Imaging systems, Cameras, Fluorescence, Neurophotonics

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DDeep3M+: adaptive enhancement powered weakly supervised learning for neuron segmentation

Rong Xiao, Lei Zhu, Jiangshan Liao, Xinglong Wu, Hui Gong, Jin Huang, Ping Li, Bin Sheng, Shangbin Chen

Neurophotonics, Vol. 10, Issue 03, 035003, (June 2023) https://doi.org/10.1117/1.NPh.10.3.035003

TOPICS: Image segmentation, Neurons, Education and training, Machine learning, Voxels, Image enhancement, 3D image processing, Deep learning, Matrices, Tunable filters

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Comparing the segmentation of quantitative phase images of neurons using convolutional neural networks trained on simulated and augmented imagery

Eddie Gil, Zachary Steelman, Anna Sedelnikova, Joel Bixler

Neurophotonics, Vol. 10, Issue 03, 035004, (June 2023) https://doi.org/10.1117/1.NPh.10.3.035004

TOPICS: Image segmentation, Education and training, Neurons, Data modeling, Performance modeling, Neurophotonics, Tunable filters, Statistical modeling, Image processing, Convolutional neural networks

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Significance

Quantitative phase imaging (QPI) can visualize cellular morphology and measure dry mass. Automated segmentation of QPI imagery is desirable for tracking neuron growth. Convolutional neural networks (CNNs) have provided state-of-the-art results for image segmentation. Improving the amount and robustness of training data is often crucial to improving CNN output on novel samples, but acquiring enough labeled data can be labor intensive. Data augmentation and simulation can be used to address this, but it is unclear whether low-complexity data can result in useful network generalization.

Aim

We trained CNNs on abstract images of neurons and on augmented images of real neurons. We then benchmarked the resulting models against human labeling.

Approach

We used a stochastic simulation of neuron growth to guide abstract QPI image and label generation. We then tested the segmentation performance of networks trained on augmented data and networks trained on simulated data against manual labeling established via consensus of three human labelers.

Results

We show that training on augmented real data resulted in a model that achieved the best Dice coefficients in our group of CNNs. The largest percent difference in dry mass estimation with respect to the ground truth was driven by segmentation errors of cell debris and phase noise. The error in dry mass when considering the cell body alone was similar between the CNNs. Neurite pixels only accounted for ∼6 % of the total image space, making them a difficult feature to learn. Future efforts should consider methods for improving neurite segmentation quality.

Conclusions

Augmented data outperformed the simulated abstract data for this testing set. The quality of segmentation of neurites was the key difference in performance between the models. Notably, even humans performed poorly when segmenting neurites. Further work is needed to improve the segmentation quality of neurites.

Impact of averaging fNIRS regional coherence data when monitoring people with long term post-concussion symptoms

Ibukunoluwa Oni, Andrew Lapointe, Bradley Goodyear, Chantel Debert, Jeff Dunn

Neurophotonics, Vol. 10, Issue 03, 035005, (July 2023) https://doi.org/10.1117/1.NPh.10.3.035005

TOPICS: Control systems, Brain, Injuries, Statistical analysis, Neurophotonics, Near infrared spectroscopy, Wavelets, Traumatic brain injury, Medicine, Displays

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Automatic and label-free detection of meningioma in dura mater using the combination of multiphoton microscopy and image analysis

Na Fang, Zanyi Wu, Rong Chen, Zhongjiang Chen, Limei Zheng, Tingqi Yang, Ling Wang, Xingfu Wang, Dezhi Kang, Jianxin Chen

Neurophotonics, Vol. 10, Issue 03, 035006, (July 2023) https://doi.org/10.1117/1.NPh.10.3.035006

TOPICS: Collagen, Second harmonic generation, Tumors, Image segmentation, Nicotinamide adenine dinucleotide, Brain tissue, Resection, Image processing, Image enhancement, Cancer detection

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Significance

To prevent meningioma recurrence, it is necessary to detect and remove all corresponding tumors intraoperatively, including those in the adjacent dura mater.

Aim

Currently, the removal of meningiomas from the dura mater depends solely on cautious visual identification of lesions by a neurosurgeon. Inspired by the requirements for resection, we propose multiphoton microscopy (MPM) based on two-photon-excited fluorescence and second-harmonic generation as a histopathological diagnostic paradigm to assist neurosurgeons in achieving precise and complete resection.

Approach

Seven fresh normal human dura mater samples and 10 meningioma-infiltrated dura mater samples, collected from 10 patients with meningioma, were acquired for this study. First, multi-channel mode and lambda mode detection were utilized in the MPM to characterize the architectural and spectral features of normal and meningioma-infiltrated dura mater, respectively. Three imaging algorithms were then employed to quantify the architectural differences between the normal and meningioma-infiltrated dura mater through calculations of the collagen content, orientation, and alignment. Finally, MPM was combined with another custom-developed imaging algorithm to locate the meningioma within the dura mater and further delineate the tumor boundary.

Results

MPM not only detected meningioma cells in the dura mater but also revealed the morphological and spectral differences between normal and meningioma-infiltrated dura mater, providing quantitative information. Furthermore, combined with a self-developed image-processing algorithm, the precise borders of meningiomas in the dura mater could be accurately delineated.

Conclusions

MPM can automatically detect meningiomas in the dura mater label-free. With the development of advanced multiphoton endoscopy, MPM combined with image analysis can provide decision-making support for histopathological diagnosis, as well as offer neurosurgeons more precise intraoperative resection guidance for meningiomas.

Toward peripheral nerve mechanical characterization using Brillouin imaging spectroscopy

Vsevolod Cheburkanov, Junwei Du, David Brogan, Mikhail Berezin, Vladislav Yakovlev

Neurophotonics, Vol. 10, Issue 03, 035007, (August 2023) https://doi.org/10.1117/1.NPh.10.3.035007

TOPICS: Nerve, Biological samples, Elasticity, Materials properties, Imaging spectroscopy, Nervous system, Spectroscopy, Injuries, Viscoelasticity, Neurons

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Enhancing diffuse correlation spectroscopy pulsatile cerebral blood flow signal with near-infrared spectroscopy photoplethysmography

Kuan Cheng Wu, Alyssa Martin, Marco Renna, Mitchell Robinson, Nisan Ozana, Stefan A. Carp, Maria Angela Franceschini

Neurophotonics, Vol. 10, Issue 03, 035008, (September 2023) https://doi.org/10.1117/1.NPh.10.3.035008

TOPICS: Near infrared spectroscopy, Signal to noise ratio, Cerebral blood flow, Blood circulation, Spectroscopy, Blood, Oxygenation, Neurophotonics, Resistance, Sampling rates

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High throughput detection of capillary stalling events with Bessel beam two-photon microscopy

John Giblin, Sreekanth Kura, Juan Luis Ugarte Nunuez, Juncheng Zhang, Gulce Kureli, John Jiang, David Boas, Ichun Chen

Neurophotonics, Vol. 10, Issue 03, 035009, (September 2023) https://doi.org/10.1117/1.NPh.10.3.035009

TOPICS: Capillaries, Bessel beams, Optical coherence tomography, Two photon excitation microscopy, Red blood cells, Neurophotonics, Animals, Angiography, Microscopes, Shadows

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Developing customized NIRS-EEG for infant sleep research: methodological considerations

Louisa Gossé, Paola Pinti, Frank Wiesemann, Clare E. Elwell, Emily J. Jones

Neurophotonics, Vol. 10, Issue 03, 035010, (September 2023) https://doi.org/10.1117/1.NPh.10.3.035010

TOPICS: Electroencephalography, Near infrared spectroscopy, Electrodes, Polysomnography, Neurophotonics, Design, Brain, Spindles, Windows, Medical research

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