Quantitative Assessment of Lung Ultrasound Grayscale Images Based on Shannon Entropy for the Detection of Pulmonary Aeration

Abstract

Objective-Lung ultrasound (LUS) is a radiation-free, affordable, and bedside monitoring method that can detect changes in pulmonary aeration before hypoxic damage. However, visual scoring methods of LUS only enable subjective diagnosis. Therefore, quantitative analysis of LUS is necessary for obtaining objective information on pulmonary aeration. Because raw data are not always available in conventional ultrasound systems, Shannon entropy (ShanEn) of information theory without the requirement of raw data is valuable. In this study, we explored the feasibility of ShanEn estimated through grayscale histogram (GSH) analysis of LUS images for the quantification of pulmonary aeration.Methods-Different degrees of pulmonary aeration caused by edema was induced in 32 male New Zealand rabbits intravenously injected with 0.1 mL/kg saline (the control group) and 0.025, 0.05, and 0.1 mL/kg oleic acid (mild, moderate, and severe groups, respectively). In vivo grayscale LUS images were acquired using a commercial point-of-care ultrasound system for estimation of GSH and corresponding ShanEn. Both lungs of each rabbit were dissected, weighed, and dried to determine the wet weight-to-dry weight ratio (W/D) through gravimetry.Results-The determination coefficients of linear correlations between ShanEn and W/D increased from 0.0487 to 0.7477 with gain and dynamic range (DR). In contrast to visual scoring methods of pulmonary aeration that use median gain and low DR, ShanEn for quantifying pulmonary aeration requires high gain and DR. Conclusion-The current findings indicate that ShanEn estimated through GSH analysis of LUS images acquired using conventional ultrasonic imaging systems has great potential to provide objective information on pulmonary aeration.

Fluorescence, ultrasonic and photoacoustic imaging for analysis and diagnosis of diseases

“…31,32 Hence, substantial efforts have been made to develop improved ultrasonic imaging methods, including ultrasensitive, quantitative, high-resolution and high-frequency ultrasonic imaging, as well as 4D functional ultrasonic imaging, aimed at improving the accuracy of tumor diagnosis. [109][110][111][112][113][114][115][116][117] By combination with other image analysis techniques (e.g., artificial intelligence (AI) and deep learning), professionals can reduce interference from false positive signals and effectively determine the boundary of tumor tissues. 31,32,115 In 2017, Dong and coworkers developed an adaptive fuzzy C-means (FCM) method based on the Hausdorff distance definition to segment the ultrasonic imaging of breast cancer by adaptive selection of adjacent regions of each pixel for distance measurement and centroid updating.…”

“…31,32 Hence, substantial efforts have been made to develop improved ultrasonic imaging methods, including ultrasensitive, quantitative, high-resolution and high-frequency ultrasonic imaging, as well as 4D functional ultrasonic imaging, aimed at improving the accuracy of tumor diagnosis. 109–117 By combination with other image analysis techniques ( e.g. , artificial intelligence (AI) and deep learning), professionals can reduce interference from false positive signals and effectively determine the boundary of tumor tissues.…”

“…31 This suggested that the developed method had the potential for computer-aided diagnosis of breast tumors and other kinds of ultrasound-guided processes. Recently, Fang et al developed a novel type of method for quantifying lung ventilation using a conventional GSUS imaging system 116. They proved that the developed Shannon entropy (namely ShanEn), estimated from an analysis of grayscale histograms of ultrasound images, has great potential to improve the quality of traditional ultrasound lung ventilation detection and provide a quantitative, non-radiation, affordable and bedside monitoring method in future potential applications.…”