Hyperspectral Imaging of Head and Neck Squamous Cell Carcinoma for Cancer Margin Detection in Surgical Specimens from 102 Patients Using Deep Learning

Halicek, Martin; Dormer, James D.; Little, James V.; Chen, Amy Y.; Myers, Larry L.; Sumer, Baran D.; Fei, Baowei

doi:10.3390/cancers11091367

Cited by 96 publications

(94 citation statements)

References 40 publications

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“…In particular, HSI has started to achieve promising results in the recent years with respect to cancer detection through the utilization of cutting-edge machine-learning algorithms [4,[19][20][21]. Several types of cancer have been investigated using HSI including both in vivo and ex vivo tissue samples, such as gastric and colon cancer [22][23][24][25], breast cancer [26,27], head and neck cancer [28][29][30][31][32][33], and brain cancer [34][35][36], among others.…”

Section: Introductionmentioning

confidence: 99%

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Martinez-Vega

Leon

Fabelo

et al. 2019

Sensors

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Hyperspectral imaging (HSI) is a non-ionizing and non-contact imaging technique capable of obtaining more information than conventional RGB (red green blue) imaging. In the medical field, HSI has commonly been investigated due to its great potential for diagnostic and surgical guidance purposes. However, the large amount of information provided by HSI normally contains redundant or non-relevant information, and it is extremely important to identify the most relevant wavelengths for a certain application in order to improve the accuracy of the predictions and reduce the execution time of the classification algorithm. Additionally, some wavelengths can contain noise and removing such bands can improve the classification stage. The work presented in this paper aims to identify such relevant spectral ranges in the visual-and-near-infrared (VNIR) region for an accurate detection of brain cancer using in vivo hyperspectral images. A methodology based on optimization algorithms has been proposed for this task, identifying the relevant wavelengths to achieve the best accuracy in the classification results obtained by a supervised classifier (support vector machines), and employing the lowest possible number of spectral bands. The results demonstrate that the proposed methodology based on the genetic algorithm optimization slightly improves the accuracy of the tumor identification in~5%, using only 48 bands, with respect to the reference results obtained with 128 bands, offering the possibility of developing customized acquisition sensors that could provide real-time HS imaging. The most relevant spectral ranges found comprise between 440. 5-465.96 nm, 498.71-509.62 nm, 556.91-575.1 nm, 593.29-615.12 nm, 636.94-666.05 nm, 698.79-731.53 nm and 884.32-902.51 nm.

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Section: Introductionmentioning

confidence: 99%

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Martinez-Vega

Leon

Fabelo

et al. 2019

Sensors

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“…In HSI digital histology, Ortega et al detected glioblastoma brain cancer in digital slides using a patch-based 2D-CNN approach [70]. Additionally, Halicek et al has employed very deep 2D-CNNs for classification, specifically the widely-used Inception v4 model (Figure 4) implemented in a sliding patch-based approach for head and neck squamous cancer [71] and thyroid and salivary gland cancers [72]. For comparing 2D-CNN and 3D-CNNs, in [73] Halicek et al explored spatial-spectral convolutions in 3D CNNs with 3D convolutional kernels to 2D approaches.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

“…In-vivo brain tumor detection ‡ [27] Deep learning 2D-CNN and 1D-DNN In-vivo brain tumor detection ‡ [69] 2D-CNN (Inception v4) Head and neck cancer [71] Salivary gland cancer [72] 2D-CNN and 3D-CNN Head and neck cancer [73] 2D-CNN (U-Net) Tongue cancer detection [74] Breast cancer [75] GAN HS image generation from RGB [76] RNNs, 2D-CNN and 3D-CNN Head and neck cancer detection [77] Publicly available datasets are marked with ‡ .…”

Section: Spatial and Spectral Features In Supervised Classificationmentioning

confidence: 99%

Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications

Ortega¹,

Halicek²,

Fabelo³

et al. 2021

Multimedia Information Retrieval

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Hyperspectral imaging (HSI) is a technology able to measure information about the spectral reflectance or transmission of light from the surface. The spectral data, usually within the ultraviolet and infrared regions of the electromagnetic spectrum, provide information about the interaction between light and different materials within the image. This fact enables the identification of different materials based on such spectral information. In recent years, this technology is being actively explored for clinical applications. One of the most relevant challenges in medical HSI is the information extraction, where image processing methods are used to extract useful information for disease detection and diagnosis. In this chapter, we provide an overview of the information extraction techniques for HSI. First, we introduce the background of HSI, and the main motivations of its usage for medical applications. Second, we present information extraction techniques based on both light propagation models within tissue and machine learning approaches. Then, we survey the usage of such information extraction techniques in HSI biomedical research applications. Finally, we discuss the main advantages and disadvantages of the most commonly used image processing approaches and the current challenges in HSI information extraction techniques in clinical applications.

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“…Otolaryngology-Head and Neck surgery is no exception. For example, studies have reported its use in diagnosing plethora of diseases of the head and neck through evaluation of patients' presenting complaint (for example, voice analysis) (7,8), or through evaluation of radiological, histological and/or endoscopic images (9)(10)(11). Other studies have reported its use in disease prognostication (for example, predicting hearing outcomes in sudden sensorineural hearing loss) (12), or in terms of predicting post-operative outcomes (for example, predicting post-operative complications in head and neck microvascular free tissue transfer) (13).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Otolaryngology-Head and Neck surgery: A Systematic Review Protocol

Lee

Mantelakis

Tailor

et al. 2020

Preprint

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Background: Machine learning describes a subfield of artificial intelligence which utilises statistical algorithms to identify patterns in large datasets. Based on previous learning, inferences or predictions can be made given novel data. Alongside its promising potential to revolutionise consumer technology, there has been growing interest in the application of machine learning algorithms to medical practice. The aim of this study is to evaluate the applications of machine learning in Otolaryngology-Head and Neck surgery.Methods: A systematic search of EMBASE, MEDLINE and CENTRAL will be conducted from January 1990 to June 2020. Studies utilising machine learning as a tool for diagnosis, or to predict disease prognosis or post-operative outcomes in the field of Otolaryngology-Head and Neck surgery will be included. The primary outcome of interest is the accuracy of machine learning models for clinical diagnosis, disease prognostication, and in predicting post-operative outcomes. This protocol adheres to the Preferred Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines.Discussion: To our knowledge, this will be the first systematic review to assimilate and critically appraise original research on the applications of machine learning across the field of Otolaryngology-Head and Neck surgery. This review has the potential to inform the current state of this technology and guide future study of machine learning approaches within the specialty.Systematic review registration: PROSPERO CRD42020192493

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Hyperspectral Imaging of Head and Neck Squamous Cell Carcinoma for Cancer Margin Detection in Surgical Specimens from 102 Patients Using Deep Learning

Cited by 96 publications

References 40 publications

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications

Machine Learning in Otolaryngology-Head and Neck surgery: A Systematic Review Protocol

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