Estimating quantitative physiological and morphological tissue parameters of murine tumor models using hyperspectral imaging and optical profilometry

Tomanič, Tadej; Rogelj, Luka; Stergar, Jošt; Markelc, Boštjan; Bozic, Tim; Kranjc, Simona; Serša, Gregor; Milanič, Matija

doi:10.1002/jbio.202200181

Cited by 8 publications

(10 citation statements)

References 73 publications

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“…After obtaining the raw hyperspectral data of the rabbit small intestine tissue, the data must be corrected to convert the raw light intensity information into relative reflectance information [40], as shown in Equation 1:

I_{norm} (λ, i, j) = \frac{I_{raw} (λ, i, j) - I_{dark} (λ, i, j)}{I_{white} (λ, i, j) - I_{dark} (λ, i, j)}

where

I_{raw} (λ, i, j)

is the light intensity value of each pixel point in the raw hyperspectral data at each band.

I_{white} (λ, i, j)

and

I_{dark} (λ, i, j)

are the light intensity value of each pixel of the whiteboard and dark in each waveband, respectively.…”

Section: Methodsmentioning

confidence: 99%

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Discrimination between normal and necrotic small intestinal tissue using hyperspectral imaging and unsupervised classification

Zhang

Huang

Chen

et al. 2023

Journal of Biophotonics

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Objective and automatic clinical discrimination of normal and necrotic sites of small intestinal tissue remains challenging. In this study, hyperspectral imaging (HSI) and unsupervised classification techniques were used to distinguish normal and necrotic sites of small intestinal tissues. Small intestinal tissue hyperspectral images of eight Japanese large‐eared white rabbits were acquired using a visible near‐infrared hyperspectral camera, and K‐means and density peaks (DP) clustering algorithms were used to differentiate between normal and necrotic tissue. The three cases in this study showed that the average clustering purity of the DP clustering algorithm reached 92.07% when the two band combinations of 500–622 and 700–858 nm were selected. The results of this study suggest that HSI and DP clustering can assist physicians in distinguishing between normal and necrotic sites in the small intestine in vivo.

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I_{norm} (λ, i, j) = \frac{I_{raw} (λ, i, j) - I_{dark} (λ, i, j)}{I_{white} (λ, i, j) - I_{dark} (λ, i, j)}

where

I_{raw} (λ, i, j)

is the light intensity value of each pixel point in the raw hyperspectral data at each band.

I_{white} (λ, i, j)

and

I_{dark} (λ, i, j)

are the light intensity value of each pixel of the whiteboard and dark in each waveband, respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

Discrimination between normal and necrotic small intestinal tissue using hyperspectral imaging and unsupervised classification

Zhang

Huang

Chen

et al. 2023

Journal of Biophotonics

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show abstract

“…Spectral signatures could help measure tissue oxygenation, 9,10 act as indicators for tumor metabolism, 2 or detect different skin pathologies 11 . Differences in the optical properties of the skin can be indicators of cancer growths 12,13 . Monte Carlo simulations can be used in combination with spectral measurements to take into account the multilayered structure of the skin, in order to provide insight into skin tissue components 14,15 …”

Section: Introductionmentioning

confidence: 99%

“…11 Differences in the optical properties of the skin can be indicators of cancer growths. 12,13 Monte Carlo simulations can be used in combination with spectral measurements to take into account the multilayered structure of the skin, in order to provide insight into skin tissue components. 14,15 HSI systems can capture color information accurately using narrowband filters and do not suffer from metamerism phenomena like RGB images.…”

Section: Introductionmentioning

confidence: 99%

Effects of dimension reduction of hyperspectral images in skin gross pathology

Aloupogianni

Ishikawa

Ichimura

et al. 2023

Skin Research and Technology

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Background: Hyperspectral imaging (HSI) is an emerging modality for the gross pathology of the skin. Spectral signatures of HSI could discriminate malignant from benign tissue. Because of inherent redundancies in HSI and in order to facilitate the use of deep-learning models, dimension reduction is a common preprocessing step.The effects of dimension reduction choice, training scope, and number of retained dimensions have not been evaluated on skin HSI for segmentation tasks. Materials and methods:An in-house dataset of HSI signatures from pigmented skin lesions was prepared and labeled with histology. Eleven different dimension reduction methods were used as preprocessing for tumor margin detection with support vector machines. Cluster-wise principal component analysis (ClusterPCA), a new variant of PCA, was proposed. The scope of application for dimension reduction was also investigated. Results:The components produced by ClusterPCA show good agreement with the expected optical properties of skin chromophores. Random forest importance performed best during classification. However, all methods suffered from low sensitivity and generalization. Conclusion:Investigation of more complex reduction and segmentation schemes with emphasis on the nature of HSI and optical properties of the skin is necessary. Insights on dimension reduction for skin tissue could facilitate the development of HSI-based systems for cancer margin detection at gross level.

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“…Tomanic et al use the hyperspectral imaging and 3D optical profilometry (OP) methods to estimate quantitative physiological and morphological tissue parameters of subcutaneous murine tumor models [3]. As described by the authors, the acquired hyperspectral images are corrected with the 3D OP method and analyzed using the inverse-adding doubling method to extract tissue properties such as melanin volume fraction and oxygenation.…”

mentioning

confidence: 99%

1st Spring Biophotonics Conference in Porto

Oliveira

Meglinski

Tuchin

2023

Journal of Biophotonics

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Estimating quantitative physiological and morphological tissue parameters of murine tumor models using hyperspectral imaging and optical profilometry

Abstract: Understanding tumors and their microenvironment are essential for successful and accurate disease diagnosis. Tissue physiology and morphology are altered in

Cited by 8 publications

References 73 publications

Discrimination between normal and necrotic small intestinal tissue using hyperspectral imaging and unsupervised classification

Discrimination between normal and necrotic small intestinal tissue using hyperspectral imaging and unsupervised classification

Effects of dimension reduction of hyperspectral images in skin gross pathology

1st Spring Biophotonics Conference in Porto

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