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.
Preoperative assessment of tissue anatomy and accurate surgical planning is crucial in conjoined twin separation surgery. We developed a new method that combines three-dimensional (3D) printing, assembling, and casting to produce anatomic models of high fidelity for surgical planning. The related anatomic features of the conjoined twins were captured by computed tomography (CT), classified as five organ groups, and reconstructed as five computer models. Among these organ groups, the skeleton was produced by fused deposition modeling (FDM) using acrylonitrile-butadiene-styrene. For the other four organ groups, shell molds were prepared by FDM and cast with silica gel to simulate soft tissues, with contrast enhancement pigments added to simulate different CT and visual contrasts. The produced models were assembled, positioned firmly within a 3D printed shell mold simulating the skin boundary, and cast with transparent silica gel. The produced phantom was subject to further CT scan in comparison with that of the patient data for fidelity evaluation. Further data analysis showed that the produced model reassembled the geometric features of the original CT data with an overall mean deviation of less than 2 mm, indicating the clinical potential to use this method for surgical planning in conjoined twin separation surgery.
In this study, we proposed and designed a transmission mode polarized hyperspectral imaging microscope (PHSIM). The hyperspectral imaging (HSI) component is based on the snapscan with a hyperspectral camera. The HSI wavelength range is from 467-700 nm. Polarized light imaging is realized by the integration of two polarizers and two liquid crystal variable retarders (LCVR), which is capable of full Stokes polarimetric imaging. The new imaging device was tested for the detection of squamous cell carcinoma (SCC) in H&E stained oral tissue slides of 8 patients. One normal area and one cancerous area on each slide are selected to make the comparison. The preliminary results indicated that the spectral curves of the Stokes vector parameters (S0, S1, S2, S3) of the normal area on the H&E stained oral tissue slides are different from those of SCC in certain wavelength range. Further work is required to apply the new polarized hyperspectral imaging microscope to a large number of patient samples and to test the PHSIM system in different cancer types.
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