Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and deep learning

Zhou, Ximing; Ma, Ling; Mubarak, Hasan; Little, James V.; Chen, Amy; Myers, Larry L.; Sumer, Baran D.; Fei, Baowei

doi:10.1117/12.2614624

Cited by 4 publications

(5 citation statements)

References 35 publications

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“…On the same dataset, a standard CNN, using only spatial information achieved a 0.81 validation accuracy and 0.74 testing accuracy. The same research group also used polarized HSI together with a CNN model on H&E stained tissue slides to analyze head and neck cancer [25]. Preliminary results showed an average testing accuracy of 0.84.…”

Section: Hyperspectral Imaging In Head and Neck Cancer Pathologymentioning

confidence: 99%

Hyperspectral imaging and evaluation of surgical margins: where do we stand?

Pertzborn,

Bali,

Mühlig

et al. 2024

Current Opinion in Otolaryngology &Amp; Head &Amp; Neck Surgery

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Purpose of review To highlight the recent literature on the use of hyperspectral imaging (HSI) for cancer margin evaluation ex vivo, for head and neck cancer pathology and in vivo during head and neck cancer surgery. Recent findings HSI can be used ex vivo on unstained and stained tissue sections to analyze head and neck tissue and tumor cells in combination with machine learning approaches to analyze head and neck cancer cell characteristics and to discriminate the tumor border from normal tissue. Data on in vivo applications during head and neck cancer surgery are preliminary and limited. Even now an accuracy of 80% for tumor versus nonneoplastic tissue classification can be achieved for certain tasks, within the current in vivo settings. Summary Significant progress has been made to introduce HSI for ex vivo head and neck cancer pathology evaluation and for an intraoperative use to define the tumor margins. To optimize the accuracy for in vivo use, larger HSI databases with annotations for head and neck cancer are needed.

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Section: Hyperspectral Imaging In Head and Neck Cancer Pathologymentioning

confidence: 99%

Hyperspectral imaging and evaluation of surgical margins: where do we stand?

Pertzborn,

Bali,

Mühlig

et al. 2024

Current Opinion in Otolaryngology &Amp; Head &Amp; Neck Surgery

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“…The setup of our polarized hyperspectral microscope has been described in our previous works [21][22][23][24][25][26]. The system acquires the images of the four Stokes parameters (S0, S1, S2, and S3) in the wavelength range from 470 nm to 900 nm.…”

Section: Polarized Hyperspectral Imagingmentioning

confidence: 99%

“…Research on PHSI for histopathological image classification is new. Our group has performed preliminary studies focused on PHSI for spectral analysis of head and neck cancer samples [21][22][23][24], white blood cells [25], and cardiac fibers [26].…”

Section: Introductionmentioning

confidence: 99%

An ensemble learning method for detection of head and neck squamous cell carcinoma using polarized hyperspectral microscopic imaging

Mubarak,

Zhou,

Palsgrove

et al. 2024

Medical Imaging 2024: Digital and Computational Pathology

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Head and neck squamous cell carcinoma (HNSCC) has a high mortality rate. In this study, we developed a Stokes-vectorderived polarized hyperspectral imaging (PHSI) system for H&E-stained pathological slides with HNSCC and built a dataset to develop a deep learning classification method based on convolutional neural networks (CNN). We use our polarized hyperspectral microscope to collect the four Stokes parameter hypercubes (S0, S1, S2, and S3) from 56 patients and synthesize pseudo-RGB images using a transformation function that approximates the human eye's spectral response to visual stimuli. Each image is divided into patches. Data augmentation is applied using rotations and flipping. We create a four-branch model architecture where each branch is trained on one Stokes parameter individually, then we freeze the branches and fine-tune the top layers of our model to generate final predictions. Our results show high accuracy, sensitivity, and specificity, indicating that our model performed well on our dataset. Future works can improve upon these results by training on more varied data, classifying tumors based on their grade, and introducing more recent architectural techniques.

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“…Polarized hyperspectral imaging (PHSI) combines polarized light imaging with HSI to acquire the polarization state, spectrum, and spatial information of a sample. We have previously used PHSI combined with machine learning to detect head and neck cancer on tissue slides [24,25,26,27], and used PHSI for cardiac fiber imaging [28].…”

Section: Introductionmentioning

confidence: 99%

Polarized Hyperspectral Microscopic Imaging for Zebrafish

Zhou,

Mubarak,

Kaur

et al. 2024

Multimodal Biomedical Imaging XIX

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Zebrafish is a well-established animal model for developmental and disease studies. Its optical transparency at early developmental stages is ideal for tissue visualization. Interaction of light with zebrafish tissues provides information on their structure and properties. In this study, we developed a microscopic imaging system for improving the visualization of unstained zebrafish tissues on tissue slides, with two different setups: polarized light imaging and polarized hyperspectral imaging. Based on the polarized light imaging setup, we collected the RGB images of Stokes vector parameters (S0, S1, S2, and S3), and calculated the Stokes vector derived parameters: the degree of polarization (DOP), the degree of linear polarization (DOLP)). We also calculated Stokes vector data based on the polarized hyperspectral imaging setup. The preliminary results demonstrate that Stokes vector data in two imaging setups (polarized light imaging and polarized hyperspectral imaging) are capable of improving the visualization of different types of zebrafish tissues (brain, muscle, skin cells, blood vessels, and yolk). Using the images collected from larval zebrafish samples by polarized light imaging, we found that DOP and DOLP could show clearer structural information of the brain and of skin cells, muscle and blood vessels in the tail. Furthermore, DOP and DOLP parameters derived from images collected by polarized hyperspectral imaging could show clearer structural information of skin cells developing around yolk as well as the surrounding blood vessel network. In addition, polarized hyperspectral imaging could provide complementary spectral information to the spatial information on Stokes vector data of zebrafish tissues. The polarized light imaging & polarized hyperspectral imaging systems provide a better insight into the microstructures of zebrafish tissues.

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Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and deep learning

Cited by 4 publications

References 35 publications

Hyperspectral imaging and evaluation of surgical margins: where do we stand?

Hyperspectral imaging and evaluation of surgical margins: where do we stand?

An ensemble learning method for detection of head and neck squamous cell carcinoma using polarized hyperspectral microscopic imaging

Polarized Hyperspectral Microscopic Imaging for Zebrafish

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