Abstract
Measuring morphological and biochemical features of tissue is crucial for disease diagnosis and surgical guidance, providing clinically significant information related to pathophysiology. Hyperspectral imaging (HSI) techniques obtain both spatial and spectral features of tissue without labeling molecules such as fluorescent dyes, which provides rich information for improved disease diagnosis and treatment. Recent advances in HSI systems have demonstrated its potential for clinical applications, especially in disease diagnosis and image-guided surgery. This review summarizes the basic principle of HSI and optical systems, deep-learning-based image analysis, and clinical applications of HSI to provide insight into this rapidly growing field of research. In addition, the challenges facing the clinical implementation of HSI techniques are discussed.
Highlights
Recent advances in optical imaging methods have improved the accuracy of disease diagnosis and surgical operation under clinical conditions [1]
The recent trends of clinical Hyperspectral imaging (HSI) methods suggest that morphological and biochemical information may play a significant role in clinical practice, saving the time and resources required for histological examination
Histological examination puts most of the time on preparing a sample with sectioning, staining, and imaging
Summary
Recent advances in optical imaging methods have improved the accuracy of disease diagnosis and surgical operation under clinical conditions [1]. The spectrally resolved imaging method has recently demonstrated a wealth of possibilities for disease diagnostic and surgical tools in clinics [7, 8]. This technique could capture the morphological and biochemical characteristics required for accurate disease diagnosis or functional analysis during a surgical operation.
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