Trends in Deep Learning for Medical Hyperspectral Image Analysis

Khan, Uzair; Sidike, Paheding; Elkin, Colin; Devabhaktuni, V.K.

doi:10.1109/access.2021.3068392

Cited by 44 publications

(20 citation statements)

References 85 publications

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“…More deep learning concepts that are relevant and applicable to medical HS images analysis are reviewed in Ref. ( Khan et al, 2021 ). The emergence of deep learning has given rise to more advanced feature extraction techniques by combining spatial and spectral information.…”

Section: Basic Knowledge Of Hyperspectral Image Systemmentioning

confidence: 99%

Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery

Yang

et al. 2022

Front. Bioeng. Biotechnol.

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The study of brain science is vital to human health. The application of hyperspectral imaging in biomedical fields has grown dramatically in recent years due to their unique optical imaging method and multidimensional information acquisition. Hyperspectral imaging technology can acquire two-dimensional spatial information and one-dimensional spectral information of biological samples simultaneously, covering the ultraviolet, visible and infrared spectral ranges with high spectral resolution, which can provide diagnostic information about the physiological, morphological and biochemical components of tissues and organs. This technology also presents finer spectral features for brain imaging studies, and further provides more auxiliary information for cerebral disease research. This paper reviews the recent advance of hyperspectral imaging in cerebral diagnosis. Firstly, the experimental setup, image acquisition and pre-processing, and analysis methods of hyperspectral technology were introduced. Secondly, the latest research progress and applications of hyperspectral imaging in brain tissue metabolism, hemodynamics, and brain cancer diagnosis in recent years were summarized briefly. Finally, the limitations of the application of hyperspectral imaging in cerebral disease diagnosis field were analyzed, and the future development direction was proposed.

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Section: Basic Knowledge Of Hyperspectral Image Systemmentioning

confidence: 99%

Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery

Yang

et al. 2022

Front. Bioeng. Biotechnol.

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“…Within biomedical image analysis. Only a small number of papers address a biomedical segmentation problem based on MSI/HSI data with deep learning [33]. Even without restricting the search to deep learning-based approaches, we could only identify five related publications (with only the first three using deep learning techniques):…”

Section: Segmentation With Msi/hsi Datamentioning

confidence: 99%

Robust deep learning-based semantic organ segmentation in hyperspectral images

Seidlitz,

Sellner,

Odenthal

et al. 2021

Preprint

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Semantic image segmentation is an important prerequisite for context-awareness and autonomous robotics in surgery. The state of the art has focused on conventional RGB video data acquired during minimally invasive surgery, but full-scene semantic segmentation based on spectral imaging data and obtained during open surgery has received almost no attention to date. To address this gap in the literature, we are investigating the following research questions based on hyperspectral imaging (HSI) data of pigs acquired in an open surgery setting: (1) What is an adequate representation of HSI data for neural network-based fully automated organ segmentation, especially with respect to the spatial granularity of the data (pixels vs. superpixels vs. patches vs. full images)? ( 2) Is there a benefit of using HSI data compared to other modalities, namely RGB data and processed HSI data (e.g. tissue parameters like oxygenation), when performing semantic organ segmentation? According to a comprehensive validation study based on 506 HSI images from 20 pigs, annotated with a total of 19 classes, deep learning-based segmentation performance increases -consistently across modalities -with the spatial context of the input data. Unprocessed HSI data offers an advantage over RGB data or processed data from the camera provider, with the advantage increasing with decreasing size of the input to the neural network. Maximum performance (HSI applied to whole images) yielded a mean dice similarity coefficient (DSC) of 0.89 (standard deviation (SD) 0.04), which is in the range of the inter-rater variability (DSC of 0.89 (SD 0.07)). We conclude that HSI could become a powerful image modality for fully-automatic surgical scene understanding with many advantages over traditional imaging, including the ability to recover additional functional tissue information.

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“…Despite all of these drawbacks, tasks such as classification, detection, and segmentation have seen tremendous progress. 52 Khan et al 52 discuss different types of neural networks and deep learning methods, and give examples on how these approaches have been applied to medical HSI. Halicek et al 21 present an overview of analyses applied in cancer studies.…”

Section: Machine Learning and Artificial Intelligencementioning

confidence: 99%