Volume 27 Issue 11 | Journal of Biomedical Optics

Journal of Biomedical Optics

VOL. 27 · NO. 11 | November 2022

CONTENTS

IN THIS ISSUE

JBO Letters (1)

General (2)

Imaging (6)

Sensing (1)

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JBO Letters

Enhanced resolution optoacoustic microscopy using a picosecond high repetition rate Q-switched microchip laser

Gianni Nteroli, Giulia Messa, Manoj Dasa, Antti Penttinen, Antti Härkönen, Mircea Guina, Adrian G. Podoleanu, Stella Koutsikou, Adrian Bradu

Journal of Biomedical Optics, Vol. 27, Issue 11, 110501, (November 2022) https://doi.org/10.1117/1.JBO.27.11.110501

TOPICS: Picosecond phenomena, Equipment, Optoacoustics, Pulsed laser operation, Resolution enhancement technologies, Transducers, Microscopy, Q switched lasers, Image resolution, Acoustics

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General

Exploring uncertainty measures in convolutional neural network for semantic segmentation of oral cancer images

Bofan Song, Shaobai Li, Sumsum Sunny, Keerthi Gurushanth, Pramila Mendonca, Nirza Mukhia, Sanjana Patrick, Tyler Peterson, Shubha Gurudath, Subhashini Raghavan, Imchen Tsusennaro, Shirley Leivon, Trupti Kolur, Vivek Shetty, Vidya Bushan, Rohan Ramesh, Vijay Pillai, Petra Wilder-Smith, Amritha Suresh, Moni Abraham Kuriakose, Praveen Birur, Rongguang Liang

Journal of Biomedical Optics, Vol. 27, Issue 11, 115001, (November 2022) https://doi.org/10.1117/1.JBO.27.11.115001

TOPICS: Image segmentation, Cancer, Tumor growth modeling, Performance modeling, Convolution, Instrument modeling, Reliability, Medical research, Inspection, Skin cancer

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Significance

Oral cancer is one of the most prevalent cancers, especially in middle- and low-income countries such as India. Automatic segmentation of oral cancer images can improve the diagnostic workflow, which is a significant task in oral cancer image analysis. Despite the remarkable success of deep-learning networks in medical segmentation, they rarely provide uncertainty quantification for their output.

Aim

We aim to estimate uncertainty in a deep-learning approach to semantic segmentation of oral cancer images and to improve the accuracy and reliability of predictions.

Approach

This work introduced a UNet-based Bayesian deep-learning (BDL) model to segment potentially malignant and malignant lesion areas in the oral cavity. The model can quantify uncertainty in predictions. We also developed an efficient model that increased the inference speed, which is almost six times smaller and two times faster (inference speed) than the original UNet. The dataset in this study was collected using our customized screening platform and was annotated by oral oncology specialists.

Results

The proposed approach achieved good segmentation performance as well as good uncertainty estimation performance. In the experiments, we observed an improvement in pixel accuracy and mean intersection over union by removing uncertain pixels. This result reflects that the model provided less accurate predictions in uncertain areas that may need more attention and further inspection. The experiments also showed that with some performance compromises, the efficient model reduced computation time and model size, which expands the potential for implementation on portable devices used in resource-limited settings.

Conclusions

Our study demonstrates the UNet-based BDL model not only can perform potentially malignant and malignant oral lesion segmentation, but also can provide informative pixel-level uncertainty estimation. With this extra uncertainty information, the accuracy and reliability of the model’s prediction can be improved.

Prognostic potential and pathological validation of a diagnostic application using Raman spectroscopy in the characterization of degenerative changes in the cartilage of the humeral head

Ryuji Asaoka, Hiroshi Kiyomatsu, Hiromasa Miura, Akihiro Jono, Tomofumi Kinoshita, Masaki Takao, Takashi Katagiri, Yusuke Oshima

Journal of Biomedical Optics, Vol. 27, Issue 11, 115002, (November 2022) https://doi.org/10.1117/1.JBO.27.11.115002

TOPICS: Raman spectroscopy, Cartilage, Collagen, Diagnostics, Spectroscopy, Head, Principal component analysis, Tissues, Proteins, Statistical analysis

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Significance

Raman spectroscopy is a well-established analytical method in the fields of chemistry, industry, biology, pharmaceutics, and medicine. Previous studies have investigated optical imaging and Raman spectroscopy for osteoarthritis (OA) diagnosis in weight-bearing joints such as hip and knee joints. However, to realize early diagnosis or a curable treatment, it is still challenging to understand the correlations with intrinsic factors or patients’ background.

Aim

To elucidate the correlation between the Raman spectral features and pathological variations of human shoulder joint cartilage.

Approach

Osteoarthritic cartilage specimens excised from the humeral heads of 14 patients who underwent shoulder arthroplasty were assessed by a confocal Raman microscope and histological staining. The Raman spectroscopic dataset of degenerative cartilage was further analyzed by principal component analysis and hierarchical cluster analysis.

Results

Multivariate association of the Raman spectral data generated three major clusters. The first cluster of patients shows a relatively high Raman intensity of collagen. The second cluster displays relatively low Raman intensities of proteoglycans (PGs) and glycosaminoglycans (GAGs), whereas the third cluster shows relatively high Raman intensities of PGs and GAGs. The reduced PGs and GAGs are typical changes in OA cartilage, which have been confirmed by safranin–O staining. In contrast, the increased Raman intensities of collagen, PGs, and GAGs may reflect the instability of the cartilage matrix structure in OA patients.

Conclusions

The results obtained confirm the correlation between the Raman spectral features and pathological variations of human shoulder joint cartilage. Unsupervised machine learning methods successfully yielded a clinically meaningful classification between the shoulder OA patients. This approach not only has potential to confirm severity of cartilage defects but also to determine the origin of an individual’s OA by evaluating the cartilage quality.

Imaging

Accurate and early prediction of the wound healing outcome of burn injuries using the wavelet Shannon entropy of terahertz time-domain waveforms

Mahmoud Ebrahim Khani, Omar Osman, Zachery Harris, Andrew Chen, Juin-Wan Zhou, Adam J. Singer, Mohammad Hassan Arbab

Journal of Biomedical Optics, Vol. 27, Issue 11, 116001, (November 2022) https://doi.org/10.1117/1.JBO.27.11.116001

TOPICS: Terahertz radiation, Injuries, Wound healing, Wavelets, Scanners, Fourier transforms, Skin, Biopsy, Tissues, Image information entropy

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Algorithms and instrumentation for rapid spatial frequency domain fluorescence diffuse optical imaging

Sang Hoon Chong, Vadim Markel, Ashwin Parthasarathy, Yihong Ong, Kenneth Abramson, Frank A. Moscatelli, Arjun Yodh

Journal of Biomedical Optics, Vol. 27, Issue 11, 116002, (November 2022) https://doi.org/10.1117/1.JBO.27.11.116002

TOPICS: Luminescence, Tumors, Detection and tracking algorithms, Modulation, Tissues, Natural surfaces, Error analysis, Surgery, Reconstruction algorithms, Tissue optics

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Influence of optical aberrations on depth-specific spatial frequency domain techniques

Motasam Majedy, Nandan Das, Johannes Johansson, Rolf Saager

Journal of Biomedical Optics, Vol. 27, Issue 11, 116003, (November 2022) https://doi.org/10.1117/1.JBO.27.11.116003

TOPICS: Scattering, Absorption, Optical properties, Optical aberrations, Tissues, Spatial frequencies, Light scattering, Chromatic aberrations, Reflectivity, Optical design

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Photoplethysmographic imaging and analysis of pulsatile pressure wave in palmar artery at 10 wavelengths

Jiahong Jin, Jun Qing Lu, Cheng Chen, Ruihai Zhou, Xin-Hua Hu

Journal of Biomedical Optics, Vol. 27, Issue 11, 116004, (November 2022) https://doi.org/10.1117/1.JBO.27.11.116004

TOPICS: Arteries, Signal to noise ratio, Tissues, Photoplethysmography, Skin, Algorithm development, Independent component analysis, Cameras, Electrocardiography, Light scattering

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Assessing jugular venous compliance with optical hemodynamic imaging by modulating intrathoracic pressure

Robert Amelard, Nyan Flannigan, Courtney Patterson, Hannah Heigold, Richard Hughson, Andrew Robertson

Journal of Biomedical Optics, Vol. 27, Issue 11, 116005, (November 2022) https://doi.org/10.1117/1.JBO.27.11.116005

TOPICS: Veins, Optical imaging, Hemodynamics, Heart, Signal attenuation, Ultrasonography, Blood, Compliance, Biomedical optics, Optical sensing

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Calibration procedure for enhanced mirror artifact removal in full-range optical coherence tomography using passive quadrature demultiplexing

Xavier Attendu, Dirk Faber, Guy Lamouche, Ton van Leeuwen, Caroline Boudoux, Maxime Rivard

Journal of Biomedical Optics, Vol. 27, Issue 11, 116006, (November 2022) https://doi.org/10.1117/1.JBO.27.11.116006

TOPICS: Mirrors, Calibration, Colorimetry, Optical coherence tomography, Range imaging, Demodulation, Signal attenuation, Optical coherence, Signal to noise ratio, Signal analyzers

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Sensing

Geometry design for a fully insertable glucose biosensor with multimodal optical readout

Jesse Fine, Gerard Coté, Michael McShane

Journal of Biomedical Optics, Vol. 27, Issue 11, 117001, (November 2022) https://doi.org/10.1117/1.JBO.27.11.117001

TOPICS: Biosensors, Glucose, Design and modelling, Oxygen, Diffusion, Simulations, Phosphorescence, Biological research, Tissue optics, Modeling

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Significance

Insertable optical continuous glucose monitors (CGMs) with wearable readers are a strong option for monitoring individuals with diabetes. However, a fully insertable CGM requires a small form factor while still delivering sufficient signal to be read through tissue by an external device. Previous work has suggested that a multimodal repeating unit (barcode) approach may meet these requirements, but the biosensor geometry must be optimized to meet performance criteria.

Aim

This work details in silico trials conducted to evaluate the geometry of a fully insertable multimodal optical biosensor with respect to both optical output and species diffusion in vivo.

Approach

Monte Carlo modeling is used to evaluate the luminescent output of three presupposed biosensor designs based on size constraints for an injectable and logical placement of the bar code compartments. Specifically, the sensitivity of the luminescent output to displacement of the biosensor in the X and Y directions, overall size of the selected design, and size of an individual repeating unit are analyzed. Further, an experimentally validated multiphysics model is used to evaluate the diffusion and reaction of glucose and oxygen within the biosensor to estimate the occurrence of chemical crosstalk between the assay components.

Results

A stacked cylinder multimodal biosensor 4.4 mm in length with repeating units 0.36 mm in length was found to yield a greater luminescent output than the current “barcode” biosensor design. In addition, it was found that a biosensor with enzymatic elements does not significantly deplete glucose locally and thus does not impact the diffusion profile of glucose in adjacent compartments containing nonenzymatic assays.

Conclusions

Computational modeling was used to design the geometry of a multimodal, insertable, and optical CGM to ensure that the optical output and chemical diffusion profile are sufficient for this device to function in vivo.

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