Hyperspectral microscopic imaging for head and neck squamous cell carcinoma detection in histologic images

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

doi:10.1117/12.2581970

Cited by 11 publications

(13 citation statements)

References 20 publications

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“…For the acquisition of hyperspectral images, we used the hyperspectral microscopic imaging system that has been reported in our previous works 4 , 5 , 20 to scan the slides. The dimensions of the hyperspectral images were

, covering a wavelength range of 470 to 720 nm.…”

Section: Methodsmentioning

confidence: 99%

“…For each calibrated hyperspectral image, we synthesized an RGB image using a customized HSI-to-RGB transformation 5 and a grayscale image by calculating the average of all 87 bands. The HSI-synthesized RGB images were generated for a better visualization of the FOV of hyperspectral images but not used for the training or validation of any network.…”

Section: Methodsmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“… 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. The light absorption, scattering, and autofluorescence of these molecular components all contribute to the spectral characteristics in hyperspectral histologic images, 6 , 16 which provide a different perspective from the traditional gross-level reflectance HSI.…”

Section: Introductionmentioning

confidence: 99%

“…We also investigated whole-image histopathological cancer detection and proved the advantage of using HSI for head and neck cancers. 5 , 20 …”

Section: Introductionmentioning

confidence: 99%

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

Rathgeb

Mubarak

et al. 2022

J. Biomed. Opt.

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. Significance: Hyperspectral imaging (HSI) provides rich spectral information for improved histopathological cancer detection. However, acquiring high-resolution HSI data for whole-slide imaging (WSI) can be time-consuming and requires a huge amount of storage space. Aim: WSI using a color camera can be achieved with fast speed, high image resolution, and excellent image quality due to the established techniques. We aim to develop an RGB-guided unsupervised hyperspectral super-resolution reconstruction method that is hypothesized to improve image quality while maintaining the spectral characteristics. Approach: High-resolution hyperspectral images of 32 histologic slides were obtained via automated WSI. High-resolution RGB histology images were registered to the hyperspectral images for RGB guidance. An unsupervised super-resolution network was trained to take the downsampled low-resolution hyperspectral patches (LR-HSI) and high-resolution RGB patches (HR-RGB) as inputs to reconstruct high-resolution hyperspectral patches (HR-HSI). Then, an Inception-based network was trained with the HR-RGB, original HR-HSI, and generated HR-HSI, respectively, for whole-slide histopathological cancer detection. Results: Our super-resolution reconstruction network generated high-resolution hyperspectral images with well-maintained spectral characteristics and improved image quality. Image classification using the original hyperspectral data outperformed RGB because of the extra spectral information. The generated hyperspectral image patches further improved the results. Conclusions: The proposed method potentially reduces image acquisition time, saves storage space without compromising image quality, and improves the image classification performance.

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, covering a wavelength range of 470 to 720 nm.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…We also investigated whole-image histopathological cancer detection and proved the advantage of using HSI for head and neck cancers. 5 , 20 …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Rathgeb

Mubarak

et al. 2022

J. Biomed. Opt.

Self Cite

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Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

Wang

Liang

et al. 2023

Otolaryngol.--head neck surg.

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ObjectiveTo update the literature and provide a systematic review of image‐based artificial intelligence (AI) applications in otolaryngology, highlight its advances, and propose future challenges.Data SourcesWeb of Science, Embase, PubMed, and Cochrane Library.Review MethodsStudies written in English, published between January 2020 and December 2022. Two independent authors screened the search results, extracted data, and assessed studies.ResultsOverall, 686 studies were identified. After screening titles and abstracts, 325 full‐text studies were assessed for eligibility, and 78 studies were included in this systematic review. The studies originated from 16 countries. Among these countries, the top 3 were China (n = 29), Korea (n = 8), the United States, and Japan (n = 7 each). The most common area was otology (n = 35), followed by rhinology (n = 20), pharyngology (n = 18), and head and neck surgery (n = 5). Most applications of AI in otology, rhinology, pharyngology, and head and neck surgery mainly included chronic otitis media (n = 9), nasal polyps (n = 4), laryngeal cancer (n = 12), and head and neck squamous cell carcinoma (n = 3), respectively. The overall performance of AI in accuracy, the area under the curve, sensitivity, and specificity were 88.39 ± 9.78%, 91.91 ± 6.70%, 86.93 ± 11.59%, and 88.62 ± 14.03%, respectively.ConclusionThis state‐of‐the‐art review aimed to highlight the increasing applications of image‐based AI in otorhinolaryngology head and neck surgery. The following steps will entail multicentre collaboration to ensure data reliability, ongoing optimization of AI algorithms, and integration into real‐world clinical practice. Future studies should consider 3‐dimensional (3D)‐based AI, such as 3D surgical AI.

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Semi-supervised anomaly detection for visual quality inspection

Napoletano

Piccoli

Schettini

2021

Expert Systems with Applications

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Hyperspectral microscopic imaging for head and neck squamous cell carcinoma detection in histologic images

Cited by 11 publications

References 20 publications

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

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

Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

Semi-supervised anomaly detection for visual quality inspection

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