Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery

Lu, Guolan; Wang, Dongsheng; Qin, Xulei; Halig, Luma V.; Müller, Susan; Zhang, Hongzheng; Chen, Amy; Pogue, Brian W.; Chen, Zhuo Georgia; Fei, Baowei

doi:10.1117/1.jbo.20.12.126012

Cited by 47 publications

(53 citation statements)

References 33 publications

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“…Proflavine has also been applied for distinguishing between benign and neoplastic mucosa in the head and neck [50]. We previously demonstrated the utility of hyperspectral imaging for head and neck cancer detection in a subcutaneous cancer animal model [47, 51] and a chemically-induced oral cancer model [52]. One prominent advantage of hyperspectral imaging is that it does not require the use of an exogenous contrast agent.…”

Section: Discussionmentioning

confidence: 99%

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Tumor margin assessment of surgical tissue specimen of cancer patients using label-free hyperspectral imaging

Fei

Wang

et al. 2017

Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XV

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We are developing label-free hyperspectral imaging (HSI) for tumor margin assessment. HSI data, hypercube (x,y,λ), consists of a series of high-resolution images of the same field of view that are acquired at different wavelengths. Every pixel on the HSI image has an optical spectrum. We developed preprocessing and classification methods for HSI data. We used spectral features from HSI data for the classification of cancer and benign tissue. We collected surgical tissue specimens from 16 human patients who underwent head and neck (H&N) cancer surgery. We acquired both HSI, autofluorescence images, and fluorescence images with 2-NBDG and proflavine from the specimens. Digitized histologic slides were examined by an H&N pathologist. The hyperspectral imaging and classification method was able to distinguish between cancer and normal tissue from oral cavity with an average accuracy of 90±8%, sensitivity of 89±9%, and specificity of 91±6%. For tissue specimens from the thyroid, the method achieved an average accuracy of 94±6%, sensitivity of 94±6%, and specificity of 95±6%. Hyperspectral imaging outperformed autofluorescence imaging or fluorescence imaging with vital dye (2-NBDG or proflavine). This study suggests that label-free hyperspectral imaging has great potential for tumor margin assessment in surgical tissue specimens of H&N cancer patients. Further development of the hyperspectral imaging technology is warranted for its application in image-guided surgery.

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Section: Discussionmentioning

confidence: 99%

“…As we reported in [47], the glare detection method includes the following steps: 1) Estimate the first-order derivatives of spectral curves with a forward difference method; 2) Calculate the standard deviation (SD) of each derivative curve and generate an SD image for each hypercube. Glare pixels show higher SD than normal pixels.…”

Section: Methodsmentioning

confidence: 99%

Tumor margin assessment of surgical tissue specimen of cancer patients using label-free hyperspectral imaging

Fei

Wang

et al. 2017

Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XV

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“…7,9,11,12 The hypercube contains 91 spectral bands, ranging from 450 to 900 nm with a 5 nm spectral sampling interval.…”

Section: Methodsmentioning

confidence: 99%

Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks

Halicek

Little

Wang

et al. 2018

Optical Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2018

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Successful outcomes of surgical cancer resection necessitate negative, cancer-free surgical margins. Currently, tissue samples are sent to pathology for diagnostic confirmation. Hyperspectral imaging (HSI) is an emerging, non-contact optical imaging technique. A reliable optical method could serve to diagnose and biopsy specimens in real-time. Using convolutional neural networks (CNNs) as a tissue classifier, we developed a method to use HSI to perform an optical biopsy of ex-vivo surgical specimens, collected from 21 patients undergoing surgical cancer resection. Training and testing on samples from different patients, the CNN can distinguish squamous cell carcinoma (SCCa) from normal aerodigestive tract tissues with an area under the curve (AUC) of 0.82, 81% accuracy, 81% sensitivity, and 80% specificity. Additionally, normal oral tissues can be sub-classified into epithelium, muscle, and glandular mucosa using a decision tree method, with an average AUC of 0.94, 90% accuracy, 93% sensitivity, and 89% specificity. After separately training on thyroid tissue, the CNN differentiates between thyroid carcinoma and normal thyroid with an AUC of 0.95, 92% accuracy, 92% sensitivity, and 92% specificity. Moreover, the CNN can discriminate medullary thyroid carcinoma from benign multi-nodular goiter (MNG) with an AUC of 0.93, 87% accuracy, 88% sensitivity, and 85% specificity. Classical-type papillary thyroid carcinoma is differentiated from benign MNG with an AUC of 0.91, 86% accuracy, 86% sensitivity, and 86% specificity. Our preliminary results demonstrate that an HSI-based optical biopsy method using CNNs can provide multi-category diagnostic information for normal head-and-neck tissue, SCCa, and thyroid carcinomas. More patient data are needed in order to fully investigate the proposed technique to establish reliability and generalizability of the work.

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“…As we reported in [38], the glare detection method includes the three steps: i) Estimate the first-order derivatives of spectral curves with a forward difference method; ii) Calculate the standard deviation (SD) of each derivative curve and generate an SD image for each hypercube. Glare pixels show higher SD than normal pixels.…”

Section: Quantitative Image Analysis Methodsmentioning

confidence: 99%

Label-free hyperspectral imaging and quantification methods for surgical margin assessment of tissue specimens of cancer patients

Fei

Halicek

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Hyperspectral imaging (HSI) is a relatively new modality in medicine and can have many potential applications. In this study, we developed label-free hyperspectral imaging for tumor margin assessment. HSI data, hypercube (x,y,λ), consists of a series of images of the same field of view that are acquired at different wavelengths. Every pixel in the hypercube has an optical spectrum. We collected surgical tissue specimens from 16 human subjects who underwent head and neck (H&N) cancer surgery. We acquired both HSI, autofluorescence images, and fluorescence images with 2-NBDG and proflavine from the specimens. Digitized histologic slides were examined by an H&N pathologist. We developed image preprocessing and classification methods for HSI data and differentiate cancer from benign tissue. The hyperspectral imaging and classification method was able to distinguish between cancer and normal tissue from oral cavity with an average accuracy of 90±8%, sensitivity of 89±9%, and specificity of 91±6%. This study suggests that label-free hyperspectral imaging has great potential for surgical margin assessment in tissue specimens of H&N cancer patients. Further development of the imaging technology and quantification methods is warranted for its application in image-guided surgery.

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Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery

Cited by 47 publications

References 33 publications

Tumor margin assessment of surgical tissue specimen of cancer patients using label-free hyperspectral imaging

Tumor margin assessment of surgical tissue specimen of cancer patients using label-free hyperspectral imaging

Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks

Label-free hyperspectral imaging and quantification methods for surgical margin assessment of tissue specimens of cancer patients

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