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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 17, Iss. 18 — Aug. 31, 2009
  • pp: 16000–16016
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Automated quantification of microstructural dimensions of the human kidney using optical coherence tomography (OCT)

Qian Li, Maristela L. Onozato, Peter M. Andrews, Chao-Wei Chen, Andrew Paek, Renee Naphas, Shuai Yuan, James Jiang, Alex Cable, and Yu Chen  »View Author Affiliations


Optics Express, Vol. 17, Issue 18, pp. 16000-16016 (2009)
http://dx.doi.org/10.1364/OE.17.016000


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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.

© 2009 Optical Society of America

1. Introduction

Optical coherence tomography (OCT) is a high-resolution, cross-sectional, three-dimensional imaging modality that measures the echo delay of light to generate images [1

1. D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178–1181 (1991). [CrossRef] [PubMed]

]. Although the light scattering properties of biological tissues typically limit light penetration to less than 2 mm, this imaging depth has proven sufficient to provide valuable information about tissue pathology in a number of biomedical fields including ophthalmology [2

2. M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Archives of Ophthalmology 113, 325–332 (1995). [CrossRef] [PubMed]

4

4. M. Wojtkowski, V. Srinivasan, J. G. Fujimoto, T. Ko, J. S. Schuman, A. Kowalczyk, and J. S. Duker, “Three-dimensional retinal imaging with high-speed ultrahigh-resolution optical coherence tomography,” Ophthalmol. 112, 1734–1746 (2005). [CrossRef]

], cardiology [5

5. J. G. Fujimoto, S. A. Boppart, G. J. Tearney, B. E. Bouma, C. Pitris, and M. E. Brezinski, “High resolution in vivo intra-arterial imaging with optical coherence tomography,” Heart 82, 128–133 (1999). [PubMed]

, 6

6. I. K. Jang, B. E. Bouma, D. H. Kang, S. J. Park, S. W. Park, K. B. Seung, K. B. Choi, M. Shishkov, K. Schlendorf, E. Pomerantsev, S. L. Houser, H. T. Aretz, and G. J. Tearney, “Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound,” J. Am. College Cardiology 39, 604–609 (2002). [CrossRef]

], and gastroenterology [7

7. B. E. Bouma, G. J. Tearney, C. C. Compton, and N. S. Nishioka, “High-resolution imaging of the human esophagus and stomach in vivo using optical coherence tomography,” Gastrointestinal Endoscopy 51, 467–474 (2000). [CrossRef] [PubMed]

10

10. Y. Chen, A. D. Aguirre, P. L. Hsiung, S. Desai, P. R. Herz, M. Pedrosa, Q. Huang, M. Figueiredo, S. W. Huang, A. Koski, J. M. Schmitt, J. G. Fujimoto, and H. Mashimo, “Ultrahigh resolution optical coherence tomography of Barrett’s esophagus: preliminary descriptive clinical study correlating images with histology,” Endoscopy 39, 599–605 (2007). [CrossRef] [PubMed]

]. OCT can be interfaced with various imaging devices such as catheters, endoscopes, laparoscopes, and needles, with typical image resolutions of 1–15 µm [11

11. J. G. Fujimoto, “Optical coherence tomography for ultrahigh resolution in vivo imaging,” Nature Biotechnol. 21, 1361–1367 (2003). [CrossRef]

]. Therefore, OCT is a promising imaging modality to assess tissue pathologies in situ and in real time. In addition, image processing has come to play an important role in understanding the information content of biological tissues [12

12. K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–575 (2003). [CrossRef] [PubMed]

-15

15. Y. Chen, A. D. Aguirre, P. Hsiung, S. W. Huang, H. Mashimo, J. M. Schmitt, and J. G. Fujimoto, “Effects of Axial Resolution Improvement on Optical Coherence Tomography (OCT) Imaging of Gastrointestinal Tissues,” Opt. Express 16, 2469–2485 (2008). [CrossRef] [PubMed]

].

The most straight-forward method to quantify the tubular diameters from OCT (or other imaging modalities) images is manual measurement using calipers or partially-automated image analysis softwares (such as ImageJ). Although accurate and reproducible measurements can be obtained in this way, an obvious drawback is the extent of user interaction required for the analysis. For instance, it requires manual selection of the region-of-interest (ROI) and the tubular wall edges on the images by the operator as the first step. This procedure is very laborious and time-consuming, which precludes the possibility of analyzing large amounts of data. This is especially challenging for OCT imaging of the kidney, since individual OCT images have a field-of-view (FOV) of several millimeters while a typical human kidney has a surface area larger than 10 cm by 10 cm. To provide an accurate assessment of the entire kidney, comprehensive OCT imaging is necessary, which would involve a large number of images from various locations of the kidney. Thus, an automatic image analysis method is critical.

2. Materials and methods

2.1 Human kidney and histology

This study protocol was approved by the Institutional Review Boards (IRB) at both the University of Maryland and Georgetown University. Four donor kidneys were obtained through the Washington Regional Transplant Consortium (WRTC). Upon arrival, the kidneys were fixed by vascular perfusion with 10% neutral formalin (through the renal artery) to preserve their renal morphology. After the OCT image acquisition, the location and direction of each scanned section were marked with ink, for subsequent standard histology processing. For conventional light microscopy, 4 µm thick sections were cut, stained with hematoxylineosin (H&E), and photographed with a Nikon Eclipse 80i (Nikon, Melville, NY) attached to a digital camera Nikon DS-Fi1 (Nikon). The micrographs were obtained for comparison with OCT images.

2.2 Optical coherence tomography (OCT) imaging

This study used a high-speed high-resolution OCT system (Thorlabs Inc., NJ, USA) using swept source/Fourier domain detection that enabled three-dimensional (3D) OCT imaging in situ. The light source was a wavelength-swept laser light source generating a 100 nm full width at half maximum (FWHM) bandwidth at 1310 nm, yielding an axial resolution of 10 µm in the tissue. The laser operated at a swept rate of 16 kHz with an average output power of 12 mW. The imaging frame rate was 30 frames per second. The transverse resolution of the system was 15 µm with 4 mW of power illuminating the sample.

Figure 1 shows the overall schematic of the OCT system used in this experiment. The inset in the lower left corner shows the imaging microscope. The output of the swept laser was split into two portions: three percent was used to generate a clock signal for triggering the sample of the OCT signal on a uniformly-spaced optical frequency grid [21

21. R. Huber, M. Wojtkowski, and J. G. Fujimoto, “Fourier Domain Mode Locking (FDML): A new laser operating regime and applications for optical coherence tomography,” Opt. Express 14, 3225–3237 (2006). [CrossRef] [PubMed]

]; the remaining ninety-seven percent of the output was equally distributed to the OCT sample and reference arms. Imaging of the human kidney sample was performed by a pair of mirrors mounted to XY scanning galvanometers (Cambridge Technology, MA, USA) and a microscope objective. The OCT imaging system’s sensitivity was 97 dB.

2.3 OCT image processing and analysis

3D OCT images of the kidney measuring 3 mm by 3 mm by 2.25 mm (512×512×512 pixels) were obtained from various locations on the human kidney samples, without contact. 3D OCT images with representative microstructures were selected and compared with corresponding conventional histology. To quantitatively evaluate the OCT images and obtain diagnostic information, image processing was performed on each individual cross-sectional (XZ or YZ plane) OCT image. Figure 2 displays a general flow chart of the automatic imaging processing procedure.

Fig. 1. Schematic and photo of the OCT imaging system. FC: fiber coupler, PC: polarization controller, C: collimator, MZI: Mach-Zehnder interferometer (frequency clocks), M: mirror, BD: balanced detector, DAQ: data acquisition board, DCG: dispersion compensating glasses, OBJ: objective.
Fig. 2. General flow chart of the automatic image processing algorithm, which includes three major steps: 1) image segmentation from the raw OCT image; 2) automatic region selection (denoted by different gray values), enabling individual analysis of each isolated region; 3) finding the boundary and skeleton for each isolated region, and quantification of the local tubule/vessel diameters.

2.3.1 Overview of image analysis methods

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 [18

18. P. M. Andrews, Y. Chen, M. L. Onozato, S. W. Huang, D. C. Adler, R. A. Huber, J. Jiang, S. E. Barry, A. E. Cable, and J. G. Fujimoto, “High-resolution optical coherence tomography imaging of the living kidney,” Lab. Investigation 88, 441–449 (2008). [CrossRef]

] (Step 1 in Fig. 2).

To accurately distinguish local changes, 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 (Step 2 in Fig. 2). 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.

In this study, we focus on the quantification of tubular (or vessel) diameter. 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. To minimize the errors due to sampling, the same analysis approaches were applied to both the XZ and YZ image cross-sections, and the final dimension was calculated by averaging the values obtained from the two cross-sections (XZ and YZ). In this way, the spatially-resolved dimensional information was obtained (Step 3 in Fig. 2) and presented in 3D.

2.3.2 Segmentation of hollow structures of kidney

A 5×5 median filter was first applied to the OCT images to reduce the background speckle noise. Segmentation is then applied to the OCT images, which subdivided an image into its constituent parts to distinguish the objects of interest and the background. In this study, we used an intensity threshold to segment the OCT images. A resulting binary image g(x,y) is defined as [22

22. R. C. Gonzalez and R. E. Woods, Digital Image Processing (Prentice Hall, 2007).

]:

g(x,y)={1;f(x,y)<T0;f(x,y)T
(1)

where f(x,y) was the gray level of a point (x,y) in the original OCT images. Thus the pixel (x,y) corresponding to hollow structure (such as tubules) was labeled 1 in the segmented image, whereas pixels labeled 0 corresponded to the background (kidney parenchyma). In such a way all pixels with a gray level lower than empirical value threshold (T) were extracted from the background for each image.

2.3.3 Automatic selection of isolated ROIs

The intensity values of the segmented images g(x,y) were scanned pixel by pixel. The background intensity was 0 and each isolated ROI was 1, every time a 1 was detected, the program was triggered to fill the region. The filling process flooded the region in four directions (up, down, left and right) until reaching the boundary, i.e., encountering 0. The filling process was performed in MATLAB based on the function “encodem”. Figure 3 shows the process of segmentation followed by the automatic selection of individual isolated ROIs. The filling process filled different regions with different values (as indicated by different colors in Fig. 3) so that it could count the regions and extract each region from the image for further processes.

Fig. 3. Demonstration of automatic selection of isolated ROIs (as indicated by different colors) (Media 1). This process enables further image analysis algorithm (such as diameter quantification) being applied to a spatially-resolved ROI instead of the whole image.

2.3.4 Quantification of image features

After extraction of an individual ROI, further morphometric analysis could be performed on the region. In the present study, we focused on the estimation of the diameter of the tubular lumen. The diameter was quantified by measuring the radius, which was the minimal distance from a specific boundary pixel to the skeleton (see Fig. 4). The boundary was defined as a pixel set where the spatial neighbor of every member contains both intensity 1 and 0 pixels. The skeleton is another pixel set which represents spatially a minimally connected stroke that a region thins to [23

23. L. Lam, S.-W. Lee, and C. Y. Suen, “Thinning Methodologies-A Comprehensive Survey,” IEEE Trans. Pattern Anal. Machine Intelligence 14, 879 (1992). [CrossRef]

]. The boundary and skeleton were obtained by the MATLAB function “bwmorph”. By using these two pixel sets, the radius for every pixel (b) on the boundary (B) was defined to be:

Radius(b)=min(distsS.(b,s))
(2)

A radius was determined for every point b by finding the minimal distance between b and the skeleton set (S). This process was applied to both cylindrical and non-cylindrical (c.f., with branches) structures as illustrated in Fig. 4. For visual purposes, pixels’ intensities were rendered with a number of 2 times the associated radius (the local diameter of the feature).

Fig. 4. Illustration of segmented image (a-c) radius quantification. d, e, and f are the graphical representations of the radius quantification of the segmented images a, b and c, respectively. A limited number of radii are shown (in red color). Boundary is shown in black color, and the skeleton is shown in gray color.

3. Results

3.1 Calibration of the dimension calculation algorithm

To quantitatively assess the accuracy of the dimension calculation algorithm, we applied this algorithm to the dimensional calculation of a capillary tube phantom. By comparing the computer calculated results with manual measurements, the performance of the algorithm was validated. Figure 5(a) shows one representative cross-sectional OCT image (YZ) of a capillary tube phantom, with the associated segmented image shown in Fig. 5(b). Figure 5(c) shows the histogram of the automatic estimation of the tube radius from a total of 61 different YZ cross-sectional OCT images along X axis. The computer algorithm estimated the diameter of the capillary tube to be 126.6±8.6 µm. A human observer measured the diameter directly from the same set of OCT images, and result in 128.3±7.4 µm (Fig. 5(d)). The computer analysis result shows a slightly larger variance since the diameter is averaged from all boundary pixel measurements, while the human observer only select few edge pixels to quantify the diameter. Fig. 5(e) shows a digital microscopy image of the capillary tube. The measured diameter is 132.7±0.9 µm. The relatively larger standard deviation from the computer algorithm compared to digital microscopy is due to: 1) the OCT imaging of tube phantom (containing the scattering media) has lower contrast compared to digital microscopy imaging of tube in air; 2) OCT imaging system has lower resolution (10 µm) compared to that of digital microscopy (~1 µm). Therefore, the tube edge in OCT image is not as sharp as those in the digital microscopy, which will result in errors in segmentation. Nevertheless, the result shows that the mean of estimation obtained by the automatic computer analysis is comparable to the true tube dimension.

3.2 OCT imaging and quantification of human kidney structures

The kidney microstructures of interest, including the uriniferous tubules, vessels, and glomeruli were identified based on their distinct morphologies. Comparisons between the OCT image and the corresponding histological micrograph indicated a close match in terms of the main structural features. In addition, the resolution of the OCT images (~10 µm) was sufficient for the purpose of revealing the morphological details. Figure 6(a) shows a representative cross-sectional OCT image of the human kidney, and Fig. 6(b) is the corresponding histopathology. As seen in Fig. 6(a), tissues with high backscattering such as kidney capsule appeared bright, while low backscattering regions such as uriniferous tubular lumens appeared dark. It was clearly observable that OCT could penetrate through the kidney capsule (C) with a penetration depth of more than 800 µm. The kidney microanatomy including uriniferous tubules (T) and glomeruli (G) were also readily distinguished.

Fig. 5. (a) An OCT image of a capillary tube phantom in YZ plane. The tube (indicated by an arrow) is submerged in a highly scattering medium with 2% Intralipid. (b) Corresponding segmentation image of (a). Tube region is highlighted by green color. (c) Histogram of the estimated tube diameters from 61 different OCT cross-sectional (YZ) images along X dimension by computer analysis and a human observer (d). (e) A digital microscopy image of the capillary tube.
Fig. 6. (a) Cross-sectional OCT image of the human kidney. Uriniferous tubules (T), glomerulus (G) and the kidney capsule (C) are distinguishable. (b) Conventional histology of the associated area in the human kidney. (c) and (d) are the histograms of the uriniferous tubules diameters calculated by the computer algorithm from OCT images, and manually measurement from the histology, respectively.

Figure 6(c) shows the histogram of the tubular lumen diameter measured by the automatic algorithm described in Section 2.3. Automatic measurement gives an estimation of lumen diameter of 27.5±10.1 µm. The manual measurement of lumen diameter from the histology slide (Fig. 5(b)) gives the results of 29.5±9.2 µm (see Fig. 6(d)). This shows that the results obtained by the automatic computer analysis are comparable to that of the manual measurements of histology slide. However, the computer calculation was much faster than the manual measurements. In addition, computer-aided analysis promises to automatically analyze a large volume of data (for example, three-dimensional data) efficiently and will be very helpful for providing the clinicians with quantitative information in a timely manner.

3.3 Three-dimensional imaging visualization of human kidney

3.3.1 Human kidney case I (blood vessels)

Figure 7(a) is the three-dimensional view of the human kidney, as generated from individual cross-sectional images. Figures 7(b)–(d) shows representative images along the three orthogonal planes (XY, YZ, and XZ), respectively. Detailed kidney vascular networks were visualized in all the image planes. The OCT image data set was further segmented and analyzed to quantify the luminal diameter of the blood vessels. Figure 7(e) shows the 3D reconstructed images showing vascular trees after intensity segmentation. The segmented 3D vascular tree was reconstructed by utilizing a 3D visualization software (Amira). The morphological features of the blood vessels can be examined. Figure 7(f) shows the quantification of the representative blood vessels luminal diameters from the ROI. Figure 7(g) shows the volume histogram of the diameter (which is obtained by counting the voxel numbers associated with the specific diameter, and multiplied by the individual voxel volume, 150.9 µm3), indicating that the majority of vessel diameters range from 50 µm to 100 µm.

For those regions without any microstructures such as tubules or vessels, the light intensity decreases exponentially with depth because of light scattering effects. However, hollow microstructures such as uriniferous tubules or blood vessels alter this exponential decay pattern due to the minimal light scattering within these hollow structures. After the light passes through these structures, it continues decreasing again. This phenomenon results in relatively higher light intensity (hyperdense shadow) below some of the microstructures as shown in the cross-sectional images (Fig. 6(c) and (d)), and casts white shadows on the en face image (Fig. 6(b)).

3.3.2 Human kidney case II (uriniferous tubules)

The previously described procedures were applied to another kidney as shown in Fig. 8. As with case I, detailed kidney tubular structures were visualized in all image planes (Fig. 8(a)–(d)). Figure 8(e) shows the 3D reconstructed images of the tubular network after intensity segmentation, which allows comprehensive examination of morphological features and interconnectivity of the renal tubules. Figure 8(f) shows the automatic quantification of tubular diameters, which were color-coded on the structural map. The volume histogram in Fig. 8(g) indicates that most tubule luminal diameters at this region range from 20 µm to approximate 40 µm, with a mean diameter around 30 µm.

3.3.3 Human kidney case III (distended uriniferous tubules)

Case III is from a third kidney. Figures 9(a)–(d) show 3D cut-through views and the representative images along the three orthogonal planes. Two clusters of distended tubules are clearly identified on Fig. 9(e). Figure 9(f) shows the automatic quantification of all the luminal tubular diameters. The tubular lumen diameters range approximately from 30–60 µm in diameter, as shown in the volume histogram (Fig. 9(g)).

3.3.4 Human kidney case IV (glomerulus)

3.3.5 Human kidney case V (vessels, tubules, & glomeruli)

Figure 11 shows a representative region with different renal structures including blood vessels, uriniferous tubules, and glomeruli. The glomeruli are surrounded by an expanded network of uriniferous tubules and blood vessels. The diameters of the glomeruli are approximately 200 µm. This result demonstrates the capability of OCT to visualize different renal microstructures in situ.

Fig. 7. (a) 3D cut-through view of the human kidney. The blood vessels as well as the kidney parenchyma are visualized. (b–d): Representative OCT images in the XY, XZ, and YZ planes. (e) 3D volumetric image of the segmented vasculature (Media 2). (f) Automatically quantified and color-coded structural image. (g) Volume histogram of the blood vessel diameter distribution.
Fig. 8. (a) 3D cut-through view of the human kidney including uriniferous tubules and the kidney parenchyma is displayed. (b–d): Representative OCT images in the XY, XZ, and YZ planes. (e) 3D volumetric image of the segmented tubular network (Media 3). (f) Automatically quantified and color-coded structural image. (g) Volume histogram of the uriniferous tubules diameter distribution.
Fig. 9. (a) 3D cut-through view of the human kidney with distended uriniferous tubules. (b–d): Representative OCT images in the XY, XZ, and YZ planes. (e) 3D volumetric image of the segmented distended tubular network (Media 4). (f) Automatically quantified and color-coded structural image. (g) Volume histogram of the distended uriniferous tubules diameter distribution.
Fig. 10. (a) 3D cut-through enlarged view of the human kidney showing a glomerulus and tubular network. (b–d): Representative OCT images in the XY, XZ, and YZ planes. (e) 3D volumetric image of the segmented glomerulus and tubular network (Media 5). (f) Automatically quantified and color-coded structural image.
Fig. 11. (a) 3D cut-through view of the human kidney showing a glomerulus (G) surrounded by tubules (T) and vessels (V). (b–d): Representative OCT images in the XY, XZ, and YZ planes. (e) 3D volumetric image of the segmented glomeruli and tubular network (Media 6). (f) Automatically quantified and color-coded structural image.

4. Discussion

OCT is a rapidly developing imaging modality that can produce 3D imaging of tissue in situ and in real time. OCT can provide cross-sectional images which make 3D reconstruction and image processing possible. It can visualize tissue microstructure without the need for contact or tissue removal, thereby facilitating sterility and minimizing possible damage to the tissue.

We should mention that there are also limitations and challenges of applying this method in kidney imaging. In the first step (automatic segmentation), speckle from OCT images could introduce artifacts. Further improvement of imaging resolution promises to reduce the speckle sizes and would improve the algorithm performance. In addition, an intensity-based segmentation algorithm was used in this study, which is subject to the setting of threshold values. In the future, more advanced segmentation algorithms, such as marker-controlled watershed segmentation [14

14. X. Qi, Y. Pan, Z. Hu, W. Kang, J. E. Willis, K. Olowe, M. V. Sivak Jr., and A. M. Rollins, “Automated quantification of colonic crypt morphology using integrated microscopy and optical coherence tomography,” J. Biomed. Opt. 13, 054055 (2008). [CrossRef] [PubMed]

], will be investigated as well.

The quantification of tubular (or vessel) diameters (step 3) was achieved by automatic identification of the boundary and skeleton of individual ROIs. This approach was limited in its estimation of the correct tubular diameter when the imaging plane did not cut through the central axis of the tubules. However, this limitation was also shared by most, if not all, cross-sectional imaging methods. For example, histology analysis of tubular and glomeruli diameter will be subject to the same sampling limitations. In our study, 3D OCT images with two orthogonal cross-sections (XZ and YZ) were utilized to obtain an averaged estimation of the tubular dimension. The sampling limitation will be further alleviated by panning the imaging plane 180 degree to fully cover the different angles. In addition, the skeleton extraction method used in this study is based on morphological thinning, which sometimes led to unwanted branches [14

14. X. Qi, Y. Pan, Z. Hu, W. Kang, J. E. Willis, K. Olowe, M. V. Sivak Jr., and A. M. Rollins, “Automated quantification of colonic crypt morphology using integrated microscopy and optical coherence tomography,” J. Biomed. Opt. 13, 054055 (2008). [CrossRef] [PubMed]

], which tended to under-estimate the tubular diameter. However, this limitation was alleviated when a large number of boundary pixels were evaluated and presented statistically. Reasonably accurate estimations of diameters of capillary tube phantom and kidney tubules were achieved through this algorithm, as confirmed by digital microscopy and histology. To overcome these above-mentioned limitations completely, future development of 3D boundary and skeleton recognition algorithms would be an ultimate solution.

Although the present OCT system has a limited resolution of 10 µm, it is still sufficient to detect the tubules in human kidney. We observed tubular diameters range from 30–60 µm from four human kidneys after formalin fixation. From the literature, normal human proximal convoluted tubule has a diameter ~55 µm [33

33. W. F. Ganong, Review of Medical Physiology, The McGraw-Hill Companies, (2005).

]. The ultimate clinical utility of this method will be assessed by the clinical evaluation of kidney viability, where the threshold tubular diameter for viable kidney can be determined.

5. Conclusion

In summary, OCT imaging of human kidney was visualized in real time and an automatic image analysis algorithm has been developed for quantifying spatially-resolved tubular diameter as a biomarker for kidney viability. Images along the three orthogonal image-planes (XY, YZ, and XZ) in the Euclidean space were displayed sequentially. Moreover, the rendering of the images provided a 3D volumetric view. The computed microstructure sizes were then color-coded on the reconstructed images, revealing quantitative information of the kidney microanatomy. Based on the results of this study, we have demonstrated the capability of OCT imaging and automatic quantification of human kidney microanatomy. The ability of OCT to provide 3D, high resolution imaging illustrates the potential of using OCT to image donor kidney structures and to evaluate the organ’s viability, or image the responses to acute kidney injuries. Future work will involve the quantification of those parameters for different human kidneys to obtain the baseline values for diagnostic purposes, and perform OCT images for human kidney in vivo to further analyze and diagnose kidney diseases.

Acknowledgements

We thank Anik Duttaroy, Bobak Shirmahamoodi, Dennis Truong, and Dipankar Dutta for technical assistances. This work is supported in part by the Nano-Biotechnology Award of the State of Maryland, the Minta Martin Foundation, the General Research Board (GRB) Award of the University of Maryland, the University of Maryland Baltimore (UMB) and College Park (UMCP) Seed Grant Program, the Prevent Cancer Foundation, and the National Kidney Foundation of the National Capital Area.

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X. D. Li, S. A. Boppart, J. Van Dam, H. Mashimo, M. Mutinga, W. Drexler, M. Klein, C. Pitris, M. L. Krinsky, M. E. Brezinski, and J. G. Fujimoto, “Optical coherence tomography: advanced technology for the endoscopic imaging of Barrett’s esophagus,” Endoscopy 32, 921–930 (2000). [CrossRef]

10.

Y. Chen, A. D. Aguirre, P. L. Hsiung, S. Desai, P. R. Herz, M. Pedrosa, Q. Huang, M. Figueiredo, S. W. Huang, A. Koski, J. M. Schmitt, J. G. Fujimoto, and H. Mashimo, “Ultrahigh resolution optical coherence tomography of Barrett’s esophagus: preliminary descriptive clinical study correlating images with histology,” Endoscopy 39, 599–605 (2007). [CrossRef] [PubMed]

11.

J. G. Fujimoto, “Optical coherence tomography for ultrahigh resolution in vivo imaging,” Nature Biotechnol. 21, 1361–1367 (2003). [CrossRef]

12.

K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Barton, “Texture analysis of optical coherence tomography images: feasibility for tissue classification,” J. Biomed. Opt. 8, 570–575 (2003). [CrossRef] [PubMed]

13.

X. Qi, M. V. Sivak, G. Isenberg, J. E. Willis, and A. M. Rollins, “Computer-aided diagnosis of dysplasia in Barrett’s esophagus using endoscopic optical coherence tomography,” J. Biomed. Opt. 11, 044010 (2006). [CrossRef] [PubMed]

14.

X. Qi, Y. Pan, Z. Hu, W. Kang, J. E. Willis, K. Olowe, M. V. Sivak Jr., and A. M. Rollins, “Automated quantification of colonic crypt morphology using integrated microscopy and optical coherence tomography,” J. Biomed. Opt. 13, 054055 (2008). [CrossRef] [PubMed]

15.

Y. Chen, A. D. Aguirre, P. Hsiung, S. W. Huang, H. Mashimo, J. M. Schmitt, and J. G. Fujimoto, “Effects of Axial Resolution Improvement on Optical Coherence Tomography (OCT) Imaging of Gastrointestinal Tissues,” Opt. Express 16, 2469–2485 (2008). [CrossRef] [PubMed]

16.

Y. Chen, P. M. Andrews, A. D. Aguirre, J. M. Schmitt, and J. G. Fujimoto, “High-resolution three-dimensional optical coherence tomography imaging of kidney microanatomy ex vivo,” J. Biomed. Opt. 12, 034008 (2007). [CrossRef] [PubMed]

17.

P. M. Andrews, B. S. Khirabadi, and B. C. Bengs, “Using tandem scanning confocal microscopy to predict the status of donor kidneys,” Nephron 91, 148–155 (2002). [CrossRef] [PubMed]

18.

P. M. Andrews, Y. Chen, M. L. Onozato, S. W. Huang, D. C. Adler, R. A. Huber, J. Jiang, S. E. Barry, A. E. Cable, and J. G. Fujimoto, “High-resolution optical coherence tomography imaging of the living kidney,” Lab. Investigation 88, 441–449 (2008). [CrossRef]

19.

O. B. Franc, C. Stefano, V. S. Maria, G. Lorenzo, H. Yale, T. Stefano, and S. Antonio, “Automatic evaluation of arterial diameter variation from vascular echographic images,” Ultrasound Med. Biol. 27, 1621–1629 (2001). [CrossRef]

20.

V. Gemignani, F. Faita, L. Ghiadoni, E. Poggianti, and M. Demi, “A system for real-time measurement of the brachial artery diameter in B-mode ultrasound images,” IEEE transactions on Medical Imaging 26, 393–404 (2007). [CrossRef] [PubMed]

21.

R. Huber, M. Wojtkowski, and J. G. Fujimoto, “Fourier Domain Mode Locking (FDML): A new laser operating regime and applications for optical coherence tomography,” Opt. Express 14, 3225–3237 (2006). [CrossRef] [PubMed]

22.

R. C. Gonzalez and R. E. Woods, Digital Image Processing (Prentice Hall, 2007).

23.

L. Lam, S.-W. Lee, and C. Y. Suen, “Thinning Methodologies-A Comprehensive Survey,” IEEE Trans. Pattern Anal. Machine Intelligence 14, 879 (1992). [CrossRef]

24.

T. J. Hall, M. F. Insana, L. A. Harrison, and G. G. Cox, “Ultrasonic measurement of glomerular diameters in normal adult humans,” Ultrasound Med. Biol. 22, 987–997 (1996). [CrossRef] [PubMed]

25.

N. A. Nassif, B. Cense, B. H. Park, M. C. Pierce, S. H. Yun, B. E. Bouma, G. J. Tearney, T. C. Chen, and J. F. de Boer, “In vivo high-resolution video-rate spectral-domain optical coherence tomography of the human retina and optic nerve,” Opt. Express 12, 367–376 (2004). [CrossRef] [PubMed]

26.

R. A. Leitgeb, W. Drexler, A. Unterhuber, B. Hermann, T. Bajraszewski, T. Le, A. Stingl, and A. F. Fercher, “Ultrahigh resolution Fourier domain optical coherence tomography,” Opt. Express 12, 2156–2165 (2004). [CrossRef] [PubMed]

27.

M. Wojtkowski, V. J. Srinivasan, T. H. Ko, J. G. Fujimoto, A. Kowalevicz, and J. S. Duker, “Ultrahigh resolution, high speed, Fourier domain optical coherence tomography and methods for dispersion compensation,” Opt. Express 12, 2404–2422 (2004). [CrossRef] [PubMed]

28.

R. Huber, M. Wojtkowski, J. G. Fujimoto, J. Y. Jiang, and A. E. Cable, “Three-dimensional and C-mode OCT imaging with a compact, frequency swept laser source at 1300 nm,” Opt. Express 13, 10523–10538 (2005). [CrossRef] [PubMed]

29.

S. Daiman and I. Koni, “Glomerular enlargement in the progression of mesangial proliferative glomerulonephritis,” Clin. Nephrol. 49, 145–152 (1998).

30.

E. Nyberg, S. O. Bohman, and U. Berg, “Glomerular volume and renal function in children with different types of nephrotic syndrome,” Pediatr. Nephrol. 8, 285–289 (1994). [CrossRef] [PubMed]

31.

H. J. Gunderson and R. Osterby, “Glomerular size and structure in diabetes mellitus II. Late abnormalities,” Disbetologia 13, 43–48 (1977). [CrossRef]

32.

K. Moran, J. Mulhall, D. Kelly, S. Sheehan, J. Dowsett, P. Dervan, and J. M. Fitzpatrick, “Morphological changes and alterations in regional intrarenal blood flow induced by graded renal ischemia,” J. Urology 148, 463–466 (1992).

33.

W. F. Ganong, Review of Medical Physiology, The McGraw-Hill Companies, (2005).

OCIS Codes
(110.2960) Imaging systems : Image analysis
(170.3880) Medical optics and biotechnology : Medical and biological imaging
(170.4500) Medical optics and biotechnology : Optical coherence tomography

ToC Category:
Medical Optics and Biotechnology

History
Original Manuscript: June 10, 2009
Revised Manuscript: August 15, 2009
Manuscript Accepted: August 22, 2009
Published: August 25, 2009

Virtual Issues
Vol. 4, Iss. 10 Virtual Journal for Biomedical Optics

Citation
Qian Li, Maristela L. Onozato, Peter M. Andrews, Chao-Wei Chen, Andrew Paek, Renee Naphas, Shuai Yuan, James Jiang, Alex Cable, and Yu Chen, "Automated quantification of microstructural dimensions of the human kidney using optical coherence tomography (OCT)," Opt. Express 17, 16000-16016 (2009)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-17-18-16000


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References

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  10. Y. Chen, A. D. Aguirre, P. L. Hsiung, S. Desai, P. R. Herz, M. Pedrosa, Q. Huang, M. Figueiredo, S. W. Huang, A. Koski, J. M. Schmitt, J. G. Fujimoto, and H. Mashimo, "Ultrahigh resolution optical coherence tomography of Barrett's esophagus: preliminary descriptive clinical study correlating images with histology," Endoscopy 39, 599-605 (2007). [CrossRef] [PubMed]
  11. J. G. Fujimoto, "Optical coherence tomography for ultrahigh resolution in vivo imaging," Nature Biotechnol. 21, 1361-1367 (2003). [CrossRef]
  12. K. W. Gossage, T. S. Tkaczyk, J. J. Rodriguez, and J. K. Barton, "Texture analysis of optical coherence tomography images: feasibility for tissue classification," J. Biomed. Opt. 8, 570-575 (2003). [CrossRef] [PubMed]
  13. X. Qi, M. V. Sivak, G. Isenberg, J. E. Willis, and A. M. Rollins, "Computer-aided diagnosis of dysplasia in Barrett's esophagus using endoscopic optical coherence tomography," J. Biomed. Opt. 11, 044010 (2006). [CrossRef] [PubMed]
  14. X. Qi, Y. Pan, Z. Hu, W. Kang, J. E. Willis, K. Olowe, M. V. Sivak, Jr., and A. M. Rollins, "Automated quantification of colonic crypt morphology using integrated microscopy and optical coherence tomography," J. Biomed. Opt. 13, 054055 (2008). [CrossRef] [PubMed]
  15. Y. Chen, A. D. Aguirre, P. Hsiung, S. W. Huang, H. Mashimo, J. M. Schmitt, and J. G. Fujimoto, "Effects of Axial Resolution Improvement on Optical Coherence Tomography (OCT) Imaging of Gastrointestinal Tissues," Opt. Express 16, 2469-2485 (2008). [CrossRef] [PubMed]
  16. Y. Chen, P. M. Andrews, A. D. Aguirre, J. M. Schmitt, and J. G. Fujimoto, "High-resolution three-dimensional optical coherence tomography imaging of kidney microanatomy ex vivo," J. Biomed. Opt. 12, 034008 (2007). [CrossRef] [PubMed]
  17. P. M. Andrews, B. S. Khirabadi, and B. C. Bengs, "Using tandem scanning confocal microscopy to predict the status of donor kidneys," Nephron 91, 148-155 (2002). [CrossRef] [PubMed]
  18. P. M. Andrews, Y. Chen, M. L. Onozato, S. W. Huang, D. C. Adler, R. A. Huber, J. Jiang, S. E. Barry, A. E. Cable, and J. G. Fujimoto, "High-resolution optical coherence tomography imaging of the living kidney," Lab. Investigation 88, 441-449 (2008). [CrossRef]
  19. O. B. Franc, C. Stefano, V. S. Maria, G. Lorenzo, H. Yale, T. Stefano, and S. Antonio, "Automatic evaluation of arterial diameter variation from vascular echographic images," Ultrasound Med. Biol. 27, 1621-1629 (2001). [CrossRef]
  20. V. Gemignani, F. Faita, L. Ghiadoni, E. Poggianti, and M. Demi, "A system for real-time measurement of the brachial artery diameter in B-mode ultrasound images," IEEE transactions on Medical Imaging 26, 393-404 (2007). [CrossRef] [PubMed]
  21. R. Huber, M. Wojtkowski, and J. G. Fujimoto, "Fourier Domain Mode Locking (FDML): A new laser operating regime and applications for optical coherence tomography," Opt. Express 14, 3225-3237 (2006). [CrossRef] [PubMed]
  22. R. C. Gonzalez, and R. E. Woods, Digital Image Processing (Prentice Hall, 2007).
  23. L. Lam, S.-W. Lee, and C. Y. Suen, "Thinning Methodologies-A Comprehensive Survey," IEEE Trans. Pattern Anal. Machine Intelligence 14, 879 (1992). [CrossRef]
  24. T. J. Hall, M. F. Insana, L. A. Harrison, and G. G. Cox, "Ultrasonic measurement of glomerular diameters in normal adult humans," Ultrasound Med. Biol. 22, 987-997 (1996). [CrossRef] [PubMed]
  25. N. A. Nassif, B. Cense, B. H. Park, M. C. Pierce, S. H. Yun, B. E. Bouma, G. J. Tearney, T. C. Chen, and J. F. de Boer, "In vivo high-resolution video-rate spectral-domain optical coherence tomography of the human retina and optic nerve," Opt. Express 12, 367-376 (2004). [CrossRef] [PubMed]
  26. R. A. Leitgeb, W. Drexler, A. Unterhuber, B. Hermann, T. Bajraszewski, T. Le, A. Stingl, and A. F. Fercher, "Ultrahigh resolution Fourier domain optical coherence tomography," Opt. Express 12, 2156-2165 (2004). [CrossRef] [PubMed]
  27. M. Wojtkowski, V. J. Srinivasan, T. H. Ko, J. G. Fujimoto, A. Kowalevicz, and J. S. Duker, "Ultrahigh resolution, high speed, Fourier domain optical coherence tomography and methods for dispersion compensation," Opt. Express 12, 2404-2422 (2004). [CrossRef] [PubMed]
  28. R. Huber, M. Wojtkowski, J. G. Fujimoto, J. Y. Jiang, and A. E. Cable, "Three-dimensional and C-mode OCT imaging with a compact, frequency swept laser source at 1300 nm," Opt. Express 13, 10523-10538 (2005). [CrossRef] [PubMed]
  29. S. Daiman, and I. Koni, "Glomerular enlargement in the progression of mesangial proliferative glomerulonephritis," Clin. Nephrol. 49, 145-152 (1998).
  30. E. Nyberg, S. O. Bohman, and U. Berg, "Glomerular volume and renal function in children with different types of nephrotic syndrome," Pediatr. Nephrol. 8, 285-289 (1994). [CrossRef] [PubMed]
  31. Q2. H. J. Gunderson, and R. Osterby, "Glomerular size and structure in diabetes mellitus II. Late abnormalities," Disbetologia 13, 43-48 (1977). [CrossRef]
  32. K. Moran, J. Mulhall, D. Kelly, S. Sheehan, J. Dowsett, P. Dervan, and J. M. Fitzpatrick, "Morphological changes and alterations in regional intrarenal blood flow induced by graded renal ischemia," J. Urology 148, 463-466 (1992).
  33. W. F. Ganong, Review of Medical Physiology, (The McGraw-Hill Companies, 2005).

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