Tissue Classification of Breast Cancer by Hyperspectral Unmixing

Jong, Lynn-Jade; Post, Anouk L.; Veluponnar, Dinusha; Geldof, Freija; Sterenborg, Henricus J. C. M.; Ruers, Theo J.M.; Dashtbozorg, Behdad

doi:10.3390/cancers15102679

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…These images can be analyzed using machine and deep learning methods to assess the margin (surgical surface) of the resected tissue as either healthy or tumorous. Deep learning methods are well suited for hyperspectral image analysis tasks due to their ability to effectively handle complex and high-dimensional input data [ 12 , 13 , 14 , 15 , 16 ]. Moreover, they are capable of automatically extracting essential features, eliminating the need for specialized expertise when dealing with the data.…”

Section: Introductionmentioning

confidence: 99%

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Spatial and Spectral Reconstruction of Breast Lumpectomy Hyperspectral Images

Jong,

Appelman,

Sterenborg

et al. 2024

Sensors

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(1) Background: Hyperspectral imaging has emerged as a promising margin assessment technique for breast-conserving surgery. However, to be implicated intraoperatively, it should be both fast and capable of yielding high-quality images to provide accurate guidance and decision-making throughout the surgery. As there exists a trade-off between image quality and data acquisition time, higher resolution images come at the cost of longer acquisition times and vice versa. (2) Methods: Therefore, in this study, we introduce a deep learning spatial–spectral reconstruction framework to obtain a high-resolution hyperspectral image from a low-resolution hyperspectral image combined with a high-resolution RGB image as input. (3) Results: Using the framework, we demonstrate the ability to perform a fast data acquisition during surgery while maintaining a high image quality, even in complex scenarios where challenges arise, such as blur due to motion artifacts, dead pixels on the camera sensor, noise from the sensor’s reduced sensitivity at spectral extremities, and specular reflections caused by smooth surface areas of the tissue. (4) Conclusion: This gives the opportunity to facilitate an accurate margin assessment through intraoperative hyperspectral imaging.

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Section: Introductionmentioning

confidence: 99%

“…Furthermore, it has also frequently been used for cancer detection. Studies that focus on the head and neck, colon, as well as breast, show promising results, with an accuracy of up to 81% for head and neck cancer [ 21 , 22 ], 88% for colon cancer [ 21 ], and 98% for breast cancer [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Spatial and Spectral Reconstruction of Breast Lumpectomy Hyperspectral Images

Jong,

Appelman,

Sterenborg

et al. 2024

Sensors

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Brain tissue classification in hyperspectral images using multistage diffusion features and transformer

Sigger,

Nguyen,

Tozzi

2024

Journal of Microscopy

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Brain surgery is a widely practised and effective treatment for brain tumours, but accurately identifying and classifying tumour boundaries is crucial to maximise resection and avoid neurological complications. This precision in classification is essential for guiding surgical decisions and subsequent treatment planning. Hyperspectral (HS) imaging (HSI) is an emerging multidimensional optical imaging method that captures detailed spectral information across multiple wavelengths, allowing for the identification of nuanced differences in tissue composition, with the potential to enhance intraoperative tissue classification. However, current frameworks often require retraining models for each HSI to extract meaningful features, resulting in long processing times and high computational costs. Additionally, most methods utilise the deep semantic features at the end of the network for classification, ignoring the spatial details contained in the shallow features. To overcome these challenges, we propose a novel approach called MedDiffHSI, which combines diffusion and transformer techniques. Our method involves training an unsupervised learning framework based on the diffusion model to extract high‐level and low‐level spectral–spatial features from HSI. This approach eliminates the need for retraining of spectral–spatial feature learning model, thereby reducing time complexity. We then extract intermediate multistage features from different timestamps for classification using a pretrained denoising U‐Net. To fully explore and exploit the rich contextual semantics and textual information hidden in the extracted diffusion feature, we utilise a spectral–spatial attention module. This module not only learns multistage information about features at different depths, but also extracts and enhances effective information from them. Finally, we employ a supervised transformer‐based classifier with weighted majority voting (WMV) to perform the HSI classification. To validate our approach, we conduct comprehensive experiments on in vivo brain database data sets and also extend the analysis to include additional HSI data sets for breast cancer to evaluate the framework performance across different types of tissue. The results demonstrate that our framework outperforms existing approaches by using minimal training samples (5%) while achieving state‐of‐the‐art performance.

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Separating Surface Reflectance from Volume Reflectance in Medical Hyperspectral Imaging

Jong,

Post,

Geldof

et al. 2024

Diagnostics

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Hyperspectral imaging has shown great promise for diagnostic applications, particularly in cancer surgery. However, non-bulk tissue-related spectral variations complicate the data analysis. Common techniques, such as standard normal variate normalization, often lead to a loss of amplitude and scattering information. This study investigates a novel approach to address these spectral variations in hyperspectral images of optical phantoms and excised human breast tissue. Our method separates surface and volume reflectance, hypothesizing that spectral variability arises from significant variations in surface reflectance across pixels. An illumination setup was developed to measure samples with a hyperspectral camera from different axial positions but with identical zenith angles. This configuration, combined with a novel data analysis approach, allows for the estimation and separation of surface reflectance for each direction and volume reflectance across all directions. Validated with optical phantoms, our method achieved an 83% reduction in spectral variability. Its functionality was further demonstrated in excised human breast tissue. Our method effectively addresses variations caused by surface reflectance or glare while conserving surface reflectance information, which may enhance sample analysis and evaluation. It benefits samples with unknown refractive index spectra and can be easily adapted and applied across a wide range of fields where hyperspectral imaging is used.

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Tissue Classification of Breast Cancer by Hyperspectral Unmixing

Cited by 3 publications

References 38 publications

Spatial and Spectral Reconstruction of Breast Lumpectomy Hyperspectral Images

Spatial and Spectral Reconstruction of Breast Lumpectomy Hyperspectral Images

Brain tissue classification in hyperspectral images using multistage diffusion features and transformer

Separating Surface Reflectance from Volume Reflectance in Medical Hyperspectral Imaging

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