Dr. Philip P. Cheney Profile

Dr. Philip P. Cheney

Senior Scientist at Battelle Memorial Institute

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Publications (6)

SPIE Journal Paper | 7 May 2024 Open Access

Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection in breast-conserving surgery

Jeeseong Hwang, Philip Cheney, Stephen Kanick, Hanh N. Le, David McClatchy, Helen Zhang, Nian Liu, Zhan-Qian John Lu, Tae Joon Cho, Kimberly Briggman, David Allen, Wendy Wells, Brian Pogue

JBO, Vol. 29, Issue 09, 093503, (May 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.9.093503

KEYWORDS: Tissues, Biological samples, Image segmentation, Tumors, Breast, Microscopy, Printed circuit board testing, Blood, Library classification systems, Surgery

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SignificanceHyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries.AimWe expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples.ApproachBreast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the K-means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes.ResultsThe manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised K-means algorithm. The unsupervised K-means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas’ unique endmembers produced by the two methods agree with each other within <2% residual error margin.ConclusionsOur report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas’ unique endmembers used by the two methods agree to <2% residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification.

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Proceedings Article | 13 March 2024 Poster

Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection

Helen Zhang, Philip Cheney, Stephan Kanick, Hanh Le, David McClatchy, Nian Liu, John Lu, Tae Joon Cho, Kimberly Briggman, David Allen, Wendy Wells, Brian Pogue, Jeeseong Hwang

Proceedings Volume PC12833, PC128330M (2024) https://doi.org/10.1117/12.3023601

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Proceedings Article | 15 March 2017 Paper

Label-free hyperspectral dark-field microscopy for quantitative scatter imaging

Philip Cheney, David McClatchy, Stephen Kanick, Paul Lemaillet, David Allen, Daniel Samarov, Brian Pogue, Jeeseong Hwang

Proceedings Volume 10056, 1005602 (2017) https://doi.org/10.1117/12.2263336

KEYWORDS: Scattering, Monte Carlo methods, Reflectivity, Optical spheres, Microscopy, Tissues, Microscopes, Distributed interactive simulations, Objectives, Transmittance

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Proceedings Article | 26 April 2016 Presentation

Label-free hyperspectral microscopy for scatter imaging of biological processes in cells (Conference Presentation)

Jeeseong Hwang, Aniruddha Ray, Philip Cheney, Bonghwan Chon, Ji Youn Lee, Kimberly Briggman

Proceedings Volume 9700, 97000L (2016) https://doi.org/10.1117/12.2218639

KEYWORDS: Microscopy, Image processing, Scattering, Confocal microscopy, Hyperspectral imaging, 3D image processing, Scatter measurement, Biomedical optics, Nanoparticles, Cell death

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SPIE Journal Paper | 1 July 2009 Open Access

Complementary optical and nuclear imaging of caspase-3 activity using combined activatable and radio-labeled multimodality molecular probe

Hyeran Lee, Walter Akers, Philip Cheney, W. Barry Edwards, Kexiang Liang, Joseph Culver, Samuel Achilefu

JBO, Vol. 14, Issue 04, 040507, (July 2009) https://doi.org/10.1117/12.10.1117/1.3207156

KEYWORDS: Luminescence, Positron emission tomography, In vivo imaging, Radio optics, Nuclear imaging, Near infrared, Optical imaging, Tumors, Diagnostics, Tissues

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