Unsupervised super resolution network for hyperspectral histologic imaging

Ma, Ling; Rathgeb, Armand; Tran, Minh; Fei, Baowei

doi:10.1117/12.2611889

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Our method can be used to assist the diagnosis of some suspicious regions where a whole-slide classification network fails to give very precise predictions, such as the tumor-normal margin. Our lab is now actively developing an automated hyperspectral microscopic imaging system as well as hyperspectral pansharpening algorithms 44 to achieve fast whole-slide scanning. In the future, we anticipate developing an automated hyperspectral cancer detection method by implementing hyperspectral whole-slide image classification first and applying our nuclei detection method to refine the classification results in suspicious regions and tumor margins.…”

Section: Discussionmentioning

confidence: 99%

Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging

Little

Chen

et al. 2022

J. Biomed. Opt.

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Section: Discussionmentioning

confidence: 99%

Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging

Little

Chen

et al. 2022

J. Biomed. Opt.

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“…For the

, and

networks, the dimensions of both the input and generated LR-HSI would be

, and

, respectively. 36 …”

Section: Methodsmentioning

confidence: 99%

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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Unsupervised super resolution network for hyperspectral histologic imaging

Cited by 2 publications

References 22 publications

Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging

Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging

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

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