Visualization of quantitative lipid distribution in mouse liver through near-infrared hyperspectral imaging

Lipid distribution in the liver provides crucial information for diagnosing the severity of fatty liver and fatty liver-associated liver cancer. Therefore, a noninvasive, label-free, and quantitative modality is eagerly anticipated. We report near-infrared hyperspectral imaging for the quantitative visualization of lipid content in mouse liver based on partial least square regression (PLSR) and support vector regression (SVR). Analysis results indicate that SVR with standard normal variate pretreatment outperforms PLSR by achieving better root mean square error (15.3 mg/g) and higher determination coefficient (0.97). The quantitative mapping of lipid content in the mouse liver is realized using SVR. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

The study demonstrated the use of near-infrared hyperspectral imaging for quantitatively visualizing lipid content in mouse liver, offering a non-invasive method for diagnosing fatty liver. The results show that support vector regression with standard normal variate pretreatment outperforms partial least squares regression in terms of accuracy and performance.