Quantitative wavelength analysis and image classification for intraoperative cancer diagnosis with hyperspectral imaging

Lu, Guolan; Qin, Xulei; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

doi:10.1117/12.2082284

Cited by 20 publications

(26 citation statements)

References 24 publications

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“…Support vector machines (SVM) was performed with both a linear and radial basis function (RBF) kernel. 22–24 Ensemble linear discriminant analysis (LDA) was performed with up to 500 learners, using the optimal number of learners determined by cross-validation. 10 Random forest algorithm was implemented using bootstrap-aggregated (bagged) decision trees with a random subset of predictors at each decision split.…”

Section: Methodsmentioning

confidence: 99%

Tumor margin classification of head and neck cancer using hyperspectral imaging and convolutional neural networks

Halicek

Little

Wang

et al. 2018

Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling

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One of the largest factors affecting disease recurrence after surgical cancer resection is negative surgical margins. Hyperspectral imaging (HSI) is an optical imaging technique with potential to serve as a computer aided diagnostic tool for identifying cancer in gross ex-vivo specimens. We developed a tissue classifier using three distinct convolutional neural network (CNN) architectures on HSI data to investigate the ability to classify the cancer margins from ex-vivo human surgical specimens, collected from 20 patients undergoing surgical cancer resection as a preliminary validation group. A new approach for generating the HSI ground truth using a registered histological cancer margin is applied in order to create a validation dataset. The CNN-based method classifies the tumor-normal margin of squamous cell carcinoma (SCCa) versus normal oral tissue with an area under the curve (AUC) of 0.86 for inter-patient validation, performing with 81% accuracy, 84% sensitivity, and 77% specificity. Thyroid carcinoma cancer-normal margins are classified with an AUC of 0.94 for inter-patient validation, performing with 90% accuracy, 91% sensitivity, and 88% specificity. Our preliminary results on a limited patient dataset demonstrate the predictive ability of HSI-based cancer margin detection, which warrants further investigation with more patient data and additional processing techniques to optimize the proposed deep learning method.

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Section: Methodsmentioning

confidence: 99%

Tumor margin classification of head and neck cancer using hyperspectral imaging and convolutional neural networks

Halicek

Little

Wang

et al. 2018

Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling

Self Cite

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show abstract

“…15, 20–22 Sensitivity measures the proportion of tumor pixels which are correctly classified as tumor. Specificity measures the proportion of normal pixels that are correctly classified as normal.…”

Section: Experiments and Methodsmentioning

confidence: 99%

Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

Chung

Tian

et al. 2016

SPIE Proceedings

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Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.

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“…Our group has investigated the feasibility of using HSI for cancer detection in a tumor mouse model of prostate cancer [10] as well as head and neck cancer [11] [12] [13] [14] [15]. In this study, we aim to evaluate the capability of HSI for early detection of head and neck cancer in a chemically-induced cancer model.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging of neoplastic progression in a mouse model of oral carcinogenesis

Qin

Wang

et al. 2016

SPIE Proceedings

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Hyperspectral imaging (HSI) is an emerging modality for medical applications and holds great potential for noninvasive early detection of cancer. It has been reported that early cancer detection can improve the survival and quality of life of head and neck cancer patients. In this paper, we explored the possibility of differentiating between premalignant lesions and healthy tongue tissue using hyperspectral imaging in a chemical induced oral cancer animal model. We proposed a novel classification algorithm for cancer detection using hyperspectral images. The method detected the dysplastic tissue with an average area under the curve (AUC) of 0.89. The hyperspectral imaging and classification technique may provide a new tool for oral cancer detection.

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Quantitative wavelength analysis and image classification for intraoperative cancer diagnosis with hyperspectral imaging

Cited by 20 publications

References 24 publications

Tumor margin classification of head and neck cancer using hyperspectral imaging and convolutional neural networks

Tumor margin classification of head and neck cancer using hyperspectral imaging and convolutional neural networks

Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

Hyperspectral imaging of neoplastic progression in a mouse model of oral carcinogenesis

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