Hyperspectral microscopy and cluster analysis for oral cancer diagnosis

Jarman, Anneliese; Manickavasagam, Arunthathi; Hosny, Neveen A.; Festy, Frederic

doi:10.1117/12.2268013

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…18,28,29 A spectral-scanning hyperspectral microscopy system, which utilized a monochromator as the spectral-scanning component, was developed for oral cancer diagnosis. 30 Both of the abovementioned systems needed a tradeoff between system complexity and resolution.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging

Little

Chen

et al. 2022

J. Biomed. Opt.

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Section: Introductionmentioning

confidence: 99%

Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging

Little

Chen

et al. 2022

J. Biomed. Opt.

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“…Unsupervised clustering methods have been implemented for ductal cancer detection using hyperspectral imaging 4 . A hyperspectral microscopy system based on a linescanning hyperspectral camera and motorized stage was developed for brain cancer detection 5 , and a spectral-scanning-based hyperspectral microscopy system was developed for oral cancer detection 6 . However, the above systems needed a tradeoff between system complexity and resolution.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral microscopic imaging for automatic detection of head and neck squamous cell carcinoma using histologic image and machine learning

Halicek

Zhou

et al. 2020

Medical Imaging 2020: Digital Pathology

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The purpose of this study is to develop hyperspectral imaging (HSI) for automatic detection of head and neck cancer cells on histologic slides. A compact hyperspectral microscopic system is developed in this study. Histologic slides from 15 patients with squamous cell carcinoma (SCC) of the larynx and hypopharynx are imaged with the system. The proposed nuclei segmentation method based on principle component analysis (PCA) can extract most nuclei in the hyperspectral image without extracting other sub-cellular components. Both spectra-based support vector machine (SVM) and patch-based convolutional neural network (CNN) are used for nuclei classification. CNNs were trained with both hyperspectral images and pseudo RGB images of extracted nuclei, in order to evaluate the usefulness of extra information provided by hyperspectral imaging. The average accuracy of spectra-based SVM classification is 68%. The average AUC and average accuracy of the HSI patch-based CNN classification is 0.94 and 82.4%, respectively. The hyperspectral microscopic imaging and classification methods provide an automatic tool to aid pathologists in detecting SCC on histologic slides.

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“…Therefore, hyperspectral imaging can be considered to identify and diagnose pathological tissue in medical images. Jarman et al 12 built a hyperspectral microscope to capture focused and intensity corrected images with wavelength ranging from 450 to 750 nm with ∼10-nm spectral resolution and submicron spatial resolution. They showed the presence of different components from a nonabsorbent saliva droplet sample.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis of liver tumors at different stages using microscopic hyperspectral imaging technology

Wang

2018

J. Biomed. Opt.

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Liver cancer has one of the highest rates of human morbidity and mortality. However, in terms of pathology, liver cancer is traditionally clinically diagnosed based on observation of microscopic images of pathological liver sections. This paper investigates in vitro samples of rat models of bile duct carcinoma and presents a quantitative analysis method based on microscopic hyperspectral imaging technology to evaluate liver cancers at different stages. The example-based feature extraction method used in this paper mainly includes two algorithms: a morphological watershed algorithm is applied to find object and segment pathological components of pathological liver sections at different stages, and a support vector machine algorithm is implemented for liver tumor classification. Majority/minority analysis is utilized as the postclassification tool to eliminate small plaques from the preliminary classification results. Then, pseudocolor synthesis in RGB color space is used to produce the final results. The experimental results show that this method can effectively calculate the percent tumor areas in liver biopsies at different time points, that is, 3.338%, 11.952%, 15.125%, and 23.375% at 8, 12, 16, and 20 weeks, respectively. Notably, through tracking analysis, the processed results of 8-week images showed the possibility for early diagnosis of the liver tumor.

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Hyperspectral microscopy and cluster analysis for oral cancer diagnosis

Cited by 3 publications

References 5 publications

Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging

Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging

Hyperspectral microscopic imaging for automatic detection of head and neck squamous cell carcinoma using histologic image and machine learning

Quantitative analysis of liver tumors at different stages using microscopic hyperspectral imaging technology

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