@article{oai:repo.qst.go.jp:00084187,
 author = {Takuma, Sugashi and Yuki, Hiroya and Niizawa, Tomoya and Hiroyuki, Takuwa and Iwao, Kanno and Kazuto, Masamoto and Takuma, Sugashi and Hiroyuki, Takuwa and Iwao, Kanno and Kazuto, Masamoto},
 issue = {5},
 journal = {Microcirculation},
 month = {Jul},
 note = {Objective
Quantification of angiographic images with two-photon laser scanning fluorescence microscopy (2PLSM) relies on proper segmentation of the vascular images. However, the images contain inhomogeneities in the signal-to-noise ratio (SNR) arising from regional effects of light scattering and absorption. The present study developed a semiautomated quantification method for volume images of 2PLSM angiography by adjusting the binarization threshold according to local SNR along the vessel centerlines.

Methods
A phantom model made with fluorescent microbeads was used to incorporate a region-dependent binarization threshold.

Results
The recommended SNR for imaging was found to be 4.2–10.6 that provide the true size of imaged objects if the binarization threshold was fixed at 50% of SNR. However, angiographic images in the mouse cortex showed variable SNR up to 45 over the depths. To minimize the errors caused by variable SNR and a spatial extent of the imaged objects in an axial direction, the microvascular networks were three-dimensionally reconstructed based on the cross-sectional diameters measured along the vessel centerline from the XY-plane images with adapted binarization threshold. The arterial volume was relatively constant over depths of 0–500 µm, and the capillary volume (1.7% relative to the scanned volume) showed the larger volumes than the artery (0.8%) and vein (0.6%).

Conclusions
The present methods allow consistent segmentation of microvasculature by adapting the local inhomogeneity in the SNR, which will be useful for quantitative comparison of the microvascular networks, such as under disease conditions where SNR in the 2PLSM images varies over space and time.},
 title = {Three-dimensional microvascular network reconstruction from in vivo images with adaptation of the regional inhomogeneity in the signal-to-noise ratio},
 volume = {28},
 year = {2021}
}