title: Automated quantification of microstructural dimensions of the human kidney using optical coherence tomography (OCT).
abstract: Optical coherence tomography (OCT) is a rapidly emerging imaging modality that can non-invasively provide cross-sectional, high-resolution images of tissue morphology in situ and in real-time. We previously demonstrated that OCT is capable of visualizing characteristic kidney anatomic structures, including blood vessels, uriniferous tubules, glomeruli, and renal capsules on a Munich-Wistar rat model. Because the viability of a donor kidney is closely correlated with its tubular morphology, and a large amount of image datasets are expected when using OCT to scan the entire kidney to provide a global assessment of its viability, it is necessary to develop automatic image analysis methods to quantify the spatially-resolved morphometric parameters such as tubular diameter to provide potential diagnostic information. In this study, we imaged the human kidney in vitro and quantified the diameters of hollow structures such as blood vessels and uriniferous tubules automatically. The microstructures were first segmented from cross-sectional OCT images. Then the spatially-isolated region-of-interest (ROI) was automatically selected to quantify its dimension. This method enables the automatic selection and quantification of spatially-resolved morphometric parameters. The quantification accuracy was validated, and measured features are in agreement with known kidney morphology. This work can enable studies to determine the clinical utility of OCT for kidney imaging, as well as studies to evaluate kidney morphology as a biomarker for assessing kidney's viability prior to transplantation.
authors.affiliation: Fischell Department of Bioengineering and Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA.
authors.forename: Qian
authors.initials: Q
authors.lastname: Li
authors.forename: Maristela L
authors.initials: ML
authors.lastname: Onozato
authors.forename: Peter M
authors.initials: PM
authors.lastname: Andrews
authors.forename: Chao-Wei
authors.initials: CW
authors.lastname: Chen
authors.forename: Andrew
authors.initials: A
authors.lastname: Paek
authors.forename: Renee
authors.initials: R
authors.lastname: Naphas
authors.forename: Shuai
authors.initials: S
authors.lastname: Yuan
authors.forename: James
authors.initials: J
authors.lastname: Jiang
authors.forename: Alex
authors.initials: A
authors.lastname: Cable
authors.forename: Yu
authors.initials: Y
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007668:Kidney / Q000166:cytology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D051381:Rats; D017208:Rats, Wistar; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D041623:Tomography, Optical Coherence / Q000379:methods*