Label-free discrimination of lung cancer cells through mueller matrix decomposition of diffuse reflectance imaging

Shrestha, Suman; Deshpande, Aditi; Farrahi, Tannaz; Cambria, Thomas; Quang, Timothy; Majeski, Joseph B.; Ying, Nie; Zervakis, Michalis; Livanos, George; Giakos, G.C.

doi:10.1016/j.bspc.2017.05.009

Cited by 17 publications

(8 citation statements)

References 19 publications

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“…During the last decade, Giakos et al [27][28][29][30] have extensively worked on a number of approaches for the differentiation of healthy and cancerous cells, introducing label-free NIR-IR polarimetric diffuse reflectance-based cancer detection methodologies in conjunction with a wavelet and fractal analysis. Visualizing the interaction of IR with healthy and malignant (cancerous) lung cells through polarimetry under diffuse reflectance geometry, in connection with polarimetric exploratory data analysis (pEDA) [29] enables the development of robust and competent diagnostic tools. Image and signal generation through the determination of the polarization states of light proves quite effective and sustains specific benefits for a wide variety of identification and classification tasks and applications, mostly based on the intrinsic nature of optical backscattering to provide increased contrast in varying polarization conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This study operates on tissue autofluorescence imaging principles, an optical data acquisition framework that has recently shown promise as a diagnostic modality. Optical fluorescence microscopy is a promising technique for high-quality histological imaging, as it does not harm the tissue samples and saves time and labor [29]. In this experiment, the most common fluorophores within the tissue absorb blue light and re-emit a portion of this radiation at higher wavelengths (green) as a fluorescence signal.…”

Section: Proposed Tumor Visualization and Identification Frameworkmentioning

confidence: 99%

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Fluorescent Imaging and Multifusion Segmentation for Enhanced Visualization and Delineation of Glioblastomas Margins

Deshpande

Cambria

Barnes

et al. 2021

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This study investigates the potential of fluorescence imaging in conjunction with an original, fused segmentation framework for enhanced detection and delineation of brain tumor margins. By means of a test bed optical microscopy system, autofluorescence is utilized to capture gray level images of brain tumor specimens through slices, obtained at various depths from the surface, each of 10 µm thickness. The samples used in this study originate from tumor cell lines characterized as Gli36ϑEGRF cells expressing a green fluorescent protein. An innovative three-step biomedical image analysis framework is presented aimed at enhancing the contrast and dissimilarity between the malignant and the remaining tissue regions to allow for enhanced visualization and accurate extraction of tumor boundaries. The fluorescence image acquisition system implemented with an appropriate unsupervised pipeline of image processing and fusion algorithms indicates clear differentiation of tumor margins and increased image contrast. Establishing protocols for the safe administration of fluorescent protein molecules, these would be introduced into glioma tissues or cells either at a pre-surgery stage or applied to the malignant tissue intraoperatively; typical applications encompass areas of fluorescence-guided surgery (FGS) and confocal laser endomicroscopy (CLE). As a result, this image acquisition scheme could significantly improve decision-making during brain tumor resection procedures and significantly facilitate brain surgery neuropathology during operation.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Proposed Tumor Visualization and Identification Frameworkmentioning

confidence: 99%

Fluorescent Imaging and Multifusion Segmentation for Enhanced Visualization and Delineation of Glioblastomas Margins

Deshpande

Cambria

Barnes

et al. 2021

Signals

Self Cite

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“…In the literature, few works have reported full MM imaging of living or fixed single cells [ 108 , 109 , 110 , 111 ]. The difficulty in performing microscopy on these small samples is mainly linked to the diffraction limit in the linear optics regime and the difficulty to preserve the polarization in all the Fields Of View (FOVs) using microscope objectives.…”

Section: Cids Microscopy Configurationmentioning

confidence: 99%

Circular Intensity Differential Scattering for Label-Free Chromatin Characterization: A Review for Optical Microscopy

2020

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Circular Intensity Differential Scattering (CIDS) provides a differential measurement of the circular right and left polarized light and has been proven to be a gold standard label-free technique to study the molecular conformation of complex biopolymers, such as chromatin. In early works, it has been shown that the scattering component of the CIDS signal gives information from the long-range chiral organization on a scale down to 1/10th–1/20th of the excitation wavelength, leading to information related to the structure and orientation of biopolymers in situ at the nanoscale. In this paper, we review the typical methods and technologies employed for measuring this signal coming from complex macro-molecules ordering. Additionally, we include a general description of the experimental architectures employed for spectroscopic CIDS measurements, angular or spectral, and of the most recent advances in the field of optical imaging microscopy, allowing a visualization of the chromatin organization in situ.

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“…Imaging with randomly scattered light is a significant challenge with a pressing need in non-invasive biomedical diagnosis [ 1 , 2 , 3 , 4 , 5 , 6 ]. One of the more significant applications is a non-invasive functional imaging technique called diffuse optical tomography (DOT), which uses near-infrared (NIR) light to map in 3D the optical characteristics of tissue by penetrating it deeply [ 7 , 8 , 9 , 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming

2023

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Diffuse optical tomography (DOT) is a non-invasive method for detecting breast cancer; however, it struggles to produce high-quality images due to the complexity of scattered light and the limitations of traditional image reconstruction algorithms. These algorithms can be affected by boundary conditions and have a low imaging accuracy, a shallow imaging depth, a long computation time, and a high signal-to-noise ratio. However, machine learning can potentially improve the performance of DOT by being better equipped to solve inverse problems, perform regression, classify medical images, and reconstruct biomedical images. In this study, we utilized a machine learning model called “XGBoost” to detect tumors in inhomogeneous breasts and applied a post-processing technique based on genetic programming to improve accuracy. The proposed algorithm was tested using simulated DOT measurements from complex inhomogeneous breasts and evaluated using the cosine similarity metrics and root mean square error loss. The results showed that the use of XGBoost and genetic programming in DOT could lead to more accurate and non-invasive detection of tumors in inhomogeneous breasts compared to traditional methods, with the reconstructed breasts having an average cosine similarity of more than 0.97 ± 0.07 and average root mean square error of around 0.1270 ± 0.0031 compared to the ground truth.

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Label-free discrimination of lung cancer cells through mueller matrix decomposition of diffuse reflectance imaging

Cited by 17 publications

References 19 publications

Fluorescent Imaging and Multifusion Segmentation for Enhanced Visualization and Delineation of Glioblastomas Margins

Fluorescent Imaging and Multifusion Segmentation for Enhanced Visualization and Delineation of Glioblastomas Margins

Circular Intensity Differential Scattering for Label-Free Chromatin Characterization: A Review for Optical Microscopy

Machine Learning Diffuse Optical Tomography Using Extreme Gradient Boosting and Genetic Programming

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