Volume 29 Issue S2 | Journal of Biomedical Optics

Journal of Biomedical Optics

VOL. 29 · NO. S2 | June 2024

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Special Issue Honoring Gabriel Popescu, Pioneer in Biomedical Optics (5)

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Special Issue Honoring Gabriel Popescu, Pioneer in Biomedical Optics

Perspectives on label-free microscopy of heterogeneous and dynamic biological systems

Dan Pham, Amani Gillette, Jeremiah Riendeau, Kasia Wiech, Emmanuel Contreras Guzman, Rupsa Datta, Melissa Skala

Journal of Biomedical Optics, Vol. 29, Issue S2, S22702, (February 2024) https://doi.org/10.1117/1.JBO.29.S2.S22702

TOPICS: Microscopy, Tissues, Diseases and disorders, Imaging systems, Image analysis, Raman spectroscopy, Cancer, Tumors, Image segmentation, Image resolution

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Swept-source Raman spectroscopy of chemical and biological materials

Jeonggeun Song, Peter T. So, Hongki Yoo, Jeon Woong Kang

Journal of Biomedical Optics, Vol. 29, Issue S2, S22703, (April 2024) https://doi.org/10.1117/1.JBO.29.S2.S22703

TOPICS: Raman spectroscopy, Spectroscopy, Biological samples, Bandpass filters, Tissues, Tunable filters, Spectral resolution, Signal processing, Optical filters, Glucose

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Significance

Raman spectroscopy has been used as a powerful tool for chemical analysis, enabling the noninvasive acquisition of molecular fingerprints from various samples. Raman spectroscopy has proven to be valuable in numerous fields, including pharmaceutical, materials science, and biomedicine. Active research and development efforts are currently underway to bring this analytical instrument into the field, enabling in situ Raman measurements for a wider range of applications. Dispersive Raman spectroscopy using a fixed, narrowband source is a common method for acquiring Raman spectra. However, dispersive Raman spectroscopy requires a bulky spectrometer, which limits its field applicability. Therefore, there has been a tremendous need to develop a portable and sensitive Raman system.

Aim

We developed a compact swept-source Raman (SS-Raman) spectroscopy system and proposed a signal processing method to mitigate hardware limitations. We demonstrated the capabilities of the SS-Raman spectroscopy by acquiring Raman spectra from both chemical and biological samples. These spectra were then compared with Raman spectra obtained using a conventional dispersive Raman spectroscopy system.

Approach

The SS-Raman spectroscopy system used a wavelength-swept source laser (822 to 842 nm), a bandpass filter with a bandwidth of 1.5 nm, and a low-noise silicon photoreceiver. Raman spectra were acquired from various chemical samples, including phenylalanine, hydroxyapatite, glucose, and acetaminophen. A comparative analysis with the conventional dispersive Raman spectroscopy was conducted by calculating the correlation coefficients between the spectra from the SS-Raman spectroscopy and those from the conventional system. Furthermore, Raman mapping was obtained from cross-sections of swine tissue, demonstrating the applicability of the SS-Raman spectroscopy in biological samples.

Results

We developed a compact SS-Raman system and validated its performance by acquiring Raman spectra from both chemical and biological materials. Our straightforward signal processing method enhanced the quality of the Raman spectra without incurring high costs. Raman spectra in the range of 900 to 1200 cm−1 were observed for phenylalanine, hydroxyapatite, glucose, and acetaminophen. The results were validated with correlation coefficients of 0.88, 0.84, 0.87, and 0.73, respectively, compared with those obtained from dispersive Raman spectroscopy. Furthermore, we performed scans across the cross-section of swine tissue to generate a biological tissue mapping plot, providing information about the composition of swine tissue.

Conclusions

We demonstrate the capabilities of the proposed compact SS-Raman spectroscopy system by obtaining Raman spectra of chemical and biological materials, utilizing straightforward signal processing. We anticipate that the SS-Raman spectroscopy will be utilized in various fields, including biomedical and chemical applications.

Bichromatic tetraphasic full-field optical coherence microscopy

Rishyashring Iyer, Mantas Žurauskas, Yug Rao, Eric Chaney, Stephen Boppart

Journal of Biomedical Optics, Vol. 29, Issue S2, S22704, (March 2024) https://doi.org/10.1117/1.JBO.29.S2.S22704

TOPICS: Biological samples, Phase reconstruction, Optical coherence tomography, Scattering, Biological imaging, Optical coherence microscopy, Spectroscopy, Biomedical optics, Phase shifts, Beam splitters

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Perspective on quantitative phase imaging to improve precision cancer medicine

Yang Liu, Shikhar Uttam

Journal of Biomedical Optics, Vol. 29, Issue S2, S22705, (March 2024) https://doi.org/10.1117/1.JBO.29.S2.S22705

TOPICS: Cancer, Refractive index, Reflection, Biological samples, Tissues, Phase imaging, Tumors, 3D image processing, Medicine, Oncology

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Significance

Quantitative phase imaging (QPI) offers a label-free approach to non-invasively characterize cellular processes by exploiting their refractive index based intrinsic contrast. QPI captures this contrast by translating refractive index associated phase shifts into intensity-based quantifiable data with nanoscale sensitivity. It holds significant potential for advancing precision cancer medicine by providing quantitative characterization of the biophysical properties of cells and tissue in their natural states.

Aim

This perspective aims to discuss the potential of QPI to increase our understanding of cancer development and its response to therapeutics. It also explores new developments in QPI methods towards advancing personalized cancer therapy and early detection.

Approach

We begin by detailing the technical advancements of QPI, examining its implementations across transmission and reflection geometries and phase retrieval methods, both interferometric and non-interferometric. The focus then shifts to QPI’s applications in cancer research, including dynamic cell mass imaging for drug response assessment, cancer risk stratification, and in-vivo tissue imaging.

Results

QPI has emerged as a crucial tool in precision cancer medicine, offering insights into tumor biology and treatment efficacy. Its sensitivity to detecting nanoscale changes holds promise for enhancing cancer diagnostics, risk assessment, and prognostication. The future of QPI is envisioned in its integration with artificial intelligence, morpho-dynamics, and spatial biology, broadening its impact in cancer research.

Conclusions

QPI presents significant potential in advancing precision cancer medicine and redefining our approach to cancer diagnosis, monitoring, and treatment. Future directions include harnessing high-throughput dynamic imaging, 3D QPI for realistic tumor models, and combining artificial intelligence with multi-omics data to extend QPI’s capabilities. As a result, QPI stands at the forefront of cancer research and clinical application in cancer care.

CellSNAP: a fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging

Piyush Raj, Santosh Kumar Paidi, Lauren Conway, Arnab Chatterjee, Ishan Barman

Journal of Biomedical Optics, Vol. 29, Issue S2, S22706, (April 2024) https://doi.org/10.1117/1.JBO.29.S2.S22706

TOPICS: Image segmentation, 3D image processing, Image processing algorithms and systems, Evolutionary algorithms, Biological imaging, Education and training, 3D mask effects, Phase imaging, Matrices, Tomography

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Significance

Three-dimensional quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. It has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw three-dimensional (3D) tomograms is not well-developed. We focus on a critical, yet often underappreciated, step of the analysis pipeline that of 3D cell segmentation from the acquired tomograms.

Aim

We report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the 3D segmentation of QPI images.

Approach

The cell segmentation algorithm mimics the gemstone extraction process, initiating with a coarse 3D extrusion from a two-dimensional (2D) segmented mask to outline the cell structure. A 2D image is generated, and a segmentation algorithm identifies the boundary in the x-y plane. Leveraging cell continuity in consecutive z-stacks, a refined 3D segmentation, akin to fine chiseling in gemstone carving, completes the process.

Results

The CellSNAP algorithm outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2 s per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused AI-based segmentation tools.

Conclusion

Our proposed method is less memory intensive and significantly faster than existing methods. The method can be easily implemented on a student laptop. Since the approach is rule-based, there is no need to collect a lot of imaging data and manually annotate them to perform machine learning based training of the model. We envision our work will lead to broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.

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