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

Abstract

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.

Unsupervised super-resolution reconstruction of hyperspectral histology images for whole-slide imaging

“…Studies have proven the usefulness of HSI in microscopy applications. 3 – 15 One of the promising applications for this technology is employing HSI for whole-slide imaging (WSI) to aid the histopathological cancer detection of tissue samples because HSI not only provides a reproducible and quantitative diagnosis of the slides but also improves the classification results compared with RGB. 3 , 5 Although various steps such as fixation and embedding during the preparation of the histological samples may alter a few tissue features, such as the texture and the water content, lots of important molecules such as proteins are preserved in the tissue slides.…”

“…Our previous studies investigated the feasibility and usefulness of HSI for head and neck squamous cell carcinoma (SCC) nuclei detection in histologic slides. 4 , 19 The comparison between HSI, HSI-synthesized RGB, and RGB indicates that the extra spectral information from HSI can improve the outcome. We also investigated whole-image histopathological cancer detection and proved the advantage of using HSI for head and neck cancers.…”

Trends in Deep Learning for Medical Hyperspectral Image Analysis

“…Hyperspectral system for imaging of skin chromophores and blood oxygenation [79] Other Estimation of tissue oxygen saturation from RGB images and sparse hyperspectral signals based on conditional generative adversarial network [78] Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology [81] Generating hyperspectral skin cancer imagery using generative adversarial neural network [82] CNN-based model used for endoscopic image reconstruction to enhance surgical guidance [62] Classifying cancerous tissue samples from neck and head regions using CNN [35] Detection of neck and head cancerous cells via classification using CNN [55] Improvisation of CNN using kernel fusion implemented for cell classification [51] Implementation of CNN for blood cell classification [52] Two-channel CNN for solving limited-samples problem for CNN models [53] Use of CNN to detect squamous cell carcinoma between samples from different patients [65] CNN used for detection of oral cancer [57] Using specular glare in MHSI along with CNN to detect squamous cell carcinoma [66] Another study for CNN to detect squamous cell carcinoma [61] Detection of brain tumor with the aid of CNN [71] CNN Detecting carcinoma thyroid sample with the aid of CNN [60] Different CNN models compared to one another for classifying skin cancer from patient data HIS [72] CNN utilized to classify and detect squamous cell carcinoma [68] CNN used to classify and detect breast cancer cells [70] CNN implemented for detection of Glioblastoma cells from Hematoxylin & Eosin tissue sample [86] In-vivo Laryngeal cancer detection based on CNN [69] Convolutional based RetinaNet model implemented to classify and detect tumors in epithelial tissue [87] Implemented a hybrid CNN model to classify colon tumor in order to aid surgical guidance [84] Five-layer CNN applied to classify endoscopy HSI [85] Study of classification for blood and similar appearing substances from HSI with CNN,RNN, MLP [54] Proposed framework of 3D-2D CNN-based approach to classify brain tumors [58] ANN Implementation of ANN and SVM for cancerous cell HSI <...>…”

Analysis and Classification of H&E-Stained Oral Cavity Tumour Gradings Using Convolution Neural Network

“…The proposed EpithNet is used for cervical cancer epithelial segmentation using histopathology biopsy slide images. In [16] author proposed two stage head and neck squamous cell carcinoma classification using hyperspectral microscopic…”