Improved second harmonic generation and two-photon excitation fluorescence microscopy-based quantitative assessments of liver fibrosis through auto-correction and optimal sampling.

BACKGROUND

Second harmonic generation (SHG)/two-photon excited fluorescence (TPEF) microscopy is commonly used for the quantitative assessment of liver fibrosis; however, the accuracy is susceptible to sampling error and count error due to disturbances induced by some forms of collagen in liver specimens. In this study, we sought to improve the accuracy of quantitative assessments by removing the effects of this disturbing collagen and optimizing the sampling protocol.

METHODS

Large liver resection samples from 111 patients with chronic hepatitis B were scanned using SHG/TPEF microscopy with multiple adjacent images. During the quantitative assessment, we then removed SHG signals associated with three types of extraneous physiological collagen: large patches of collagen near the boundary of the capsule, collagen around tubular structures, and collagen associated with distorted vessel walls. The optimal sampling protocol was identified by comparing scans from regions of interest of various sizes (3×3 tiles and 5×5 tiles) with full scans of the same tissue.

RESULTS

The proposed auto-correction algorithm detected 88 of 97 (90.7%) disturbing collagen on the images from the validation set. Removing these signals of disturbing collagen improved the correlation between Metavir stage and quantification of all 41 proposed collagen features. Through optimal sampling, five scans of 5×5 tiles or ten scans of 3×3 tiles were sufficient to minimize the mean error rate to around 2% of collagen percentage quantification and to achieve similar correlations around 0.27 with Metavir stage as using full tissue scans.

CONCLUSIONS

Our results demonstrate that the quantitative assessments of liver fibrosis can be greatly enhanced in terms of accuracy and efficiency through optimal sampling and the automated removal of disturbing collagen signals. These types of image processing could be integrated in next-generation SHG/TPEF microscopic systems.