Feature Of The Week 9/6/09: Automated Quantification of Microstructural Dimensions of the Human Kidney using Optical Coherence Tomography
Feature Of The Week 9/6/09: 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. Recent development in high-speed OCT enables the acquisition of a large amount of three-dimensional image datasets in vivo. To handle these large datasets it is necessary to develop automatic image analysis methods for quantification of spatially-resolved information for clinical applications. Recenty Qian Li and other researchers from the University of Maryland, Georgetown University, and Thorlabs demonstrated development of an automatic algorithm to quantify the dimensions of hollow structures in human kidneys, such as blood vessels, uriniferous tubules, and glomeruli. Such information has potential to be useful for assessment of the viability of transplant organs.
The automatic image processing method included image segmentation, region-of-interest (ROI) selection, and image feature quantification. First, the raw OCT image data were obtained (XZ and YZ). The contour of kidney surface was identified by edge detection on each A-scan. Then the structures in the kidney (such as uriniferous tubules and blood vessels) were segmented from the kidney parenchyma based on their different backscattering intensities. To accurately distinguish local structures, an image processing algorithm was used to automatically identify and separate the isolated sections (i.e. uriniferous tubules) from the segmented images to quantify the diameter of each ROI (such as individual tubules or blood vessels). The algorithm systematically filled the region to the section boundary and labeled each region with a unique index. This algorithm allowed different regions to be individually selected for further morphometrical analysis (for instance, quantifying the diameter) or to count the total number of isolated sections. This step was essential to ensure that the diameters measured are from the selected ROI, therefore, can be color-coded and displayed in a spatially-resolved way. To quantify the diameter of each isolated ROI, the corresponding boundary and skeleton were generated. As a result, the diameters of each luminal position in this ROI were calculated based the average of the shortest distances from the boundary to the skeleton.
The accuracy of the dimension calculation algorithm was first assessed on tissue phantom using a capillary tube phantom. The result shows that the mean of estimation obtained by the automatic computer analysis agrees with the true tube dimension. Next, the automatic quantification of kidney tubular diameter was compared with the manual measurement of histology with comparable results. Lastly, three-dimensional examples of human kidney blood vessels, uriniferous tubules, and glomeruli are presented.
This work demonstrated an automatic image analysis algorithm for quantifying spatially-resolved tubular diameter as a biomarker for kidney viability. This algorithm can be also generalized to analyze other digital images. Future work will involve the development of advanced image analysis algorithm to automatically classify various microstructures in the kidney, and perform OCT imaging of human kidney in vivo to assess the clinical utility of OCT for diagnosis and management of kidney diseases.