Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging

Abstract

Surgical cancer resection requires an accurate and timely diagnosis of the cancer margins in order to achieve successful patient remission. Hyperspectral imaging (HSI) has emerged as a useful, noncontact technique for acquiring spectral and optical properties of tissue. A convolutional neural network (CNN) classifier is developed to classify excised, squamous-cell carcinoma, thyroid cancer, and normal head and neck tissue samples using HSI. The CNN classification was validated by the manual annotation of a pathologist specialized in head and neck cancer. The preliminary results of 50 patients indicate the potential of HSI and deep learning for automatic tissue-labeling of surgical specimens of head and neck patients.

Figures

Fig. 1

(a) Normalized reflectance curves for the average spectra, shown with standard deviation, of all 29 SCCa patients. (b) Normalized reflectance curves for the average spectra of all 21 thyroid patients.

Fig. 2

Flowchart of the data processing and deep learning architecture. The spectral signatures from 5×5 blocks extracted from the hypercube are reformatted into 10×10 spectral patches. The CNN trained on the spectral patches consisted of six convolutional layers (height, width, and filter numbers are shown) and three fully connected layers (number of neurons in the layer are shown).

Fig. 3

(a) Representative HSI-RGB composite and histological images from maxillary sinus SCCa (left) and thyroid (right) patients. The dotted line indicates the cancer margin. (b) Representative CNN classification results of a larynx SCCa patient.