Segmentation and Volumetric Measurement of Renal Cysts and Parenchyma from MR Images of Polycystic Kidneys Using Multi-spectral Analysis Method

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1 Segmentation and Volumetric Measurement of Renal Cysts and Parenchyma from MR Images of Polycystic Kidneys Using Multi-spectral Analysis Method KT Bae* a, PK Commean b, BS Brunsden b, DA Baumgarten c, BF King, Jr. d, LH Wetzel e, PJ Kenney f, AB Chapman c, VE Torres d, JJ Grantham e, LM Guay-Woodford f, C Tao a, JP Miller b, CM Meyers g, WM Bennett h, and Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) a University of Pittsburgh, Pittsburgh, PA; b Washington University, St. Louis, MO; c Emory University School of Medicine, Atlanta GA; d Mayo Foundation, Rochester, MN; e University of Kansas Medical Center, Kansas City, KS; f University of Alabama at Birmingham; g The National Institutes of Health, NIDDK; h Northwest Renal Clinic, Portland, OR ABSTRACT For segmentation and volume measurement of renal cysts and parenchyma from kidney MR images in subjects with autosomal dominant polycystic kidney disease (ADPKD), a semi-automated, multi-spectral anaylsis (MSA) method was developed and applied to T1- and T2-weighted MR images. In this method, renal cysts and parenchyma were characterized and segmented for their characteristic T1 and T2 signal intensity differences. The performance of the MSA segmentation method was tested on ADPKD phantoms and patients. Segmented renal cysts and parenchyma volumes were measured and compared with reference standard measurements by fluid displacement method in the phantoms and stereology and region-based thresholding methods in patients, respectively. As results, renal cysts and parenchyma were segmented successfully with the MSA method. The volume measurements obtained with MSA were in good agreement with the measurements by other segmentation methods for both phantoms and subjects. The MSA method, however, was more time-consuming than the other segmentation methods because it required pre-segmentation, image registration and tissue classification-determination steps. Keywords: kidney, cysts; kidney, MR, polycystic kidney disease, image segmentation, multi-spectral analysis 1. INTRODUCTION Autosomal dominant polycystic kidney disease (ADPKD) is the most common single-gene renal disorder characterized by the growth of numerous cysts in the kidneys. It is the fourth leading cause of chronic kidney disease leading to renal insufficiency in adults (1). Imaging plays a major role in the screening and management of ADPKD, since genetic tests cannot determine the severity of the disease or prevent complications (2). Characteristic imaging findings include enlarged bilateral kidneys with multiple cysts. Early in the disease normal renal parenchyma is substantially present and clearly discernible, but with time, the kidney becomes completely replaced by cysts and no normal parenchyma is identified. As the cysts become more numerous and enlarged, the kidney itself enlarges. ADPKD ultimately leads to the terminal destruction of normal renal parenchyma in more than 50% of affected patients (3-6). The absence of a sensitive measure of disease progression in the early stages of ADPKD has obstructed the development of therapeutic agents. Recent imaging studies (6-10), however, demonstrated that image-based biomarkers may be used to quantify the disease progression in ADPKD. These studies reported that renal and renal cyst volumes in *baek@upmc.edu; phone ; fax Medical Imaging 2008: Image Processing, edited by Joseph M. Reinhardt, Josien P. W. Pluim, Proc. of SPIE Vol. 6914, , (2008) /08/$18 doi: / SPIE Digital Library -- Subscriber Archive Copy Proc. of SPIE Vol

2 ADPKD kidneys can be reliably quantified from MR images and that the annual change in renal volume can be quantified and used to measure the rate of disease progression. They also found that higher rates of kidney enlargement were associated with a more rapid decrease in renal function. To measure renal and renal cyst volumes from abdominal MR images, renal and renal cyst regions should be segmented from the remaining abdominal anatomy. Although numerous segmentation techniques are available (11, 12), the choice of segmentation methods to evaluate the progress of ADPKD should be weighed on obtaining reliable volumetric measurements with high efficiency. In recent studies (7, 8, 10), the stereology method, which is a quantitative morphology method based on spatial sampling of uniformly spaced grid points placed over the object, has been used to quantify renal volumes in ADPKD, while a region-based threshold method was used to calculate cyst volumes. These methods rely heavily on the operator s understanding and identification of renal anatomy to segment and quantify renal and renal cyst volumes. As an alternative segmentation method, in this study, we have developed and tested the multi-spectral analysis (MSA) segmentation method. This method uses an intrinsic multi-spectral property of MR images and has been applied by many researchers to segment and measure anatomical structures or lesions of the brain and other organs (13-21). In this method, each pixel is displayed in a scatter plot at the location determined by the intensity of the pixel in T1- and T2- weighted images. Then, coherent regions are classified by clustering pixels in the feature space. To our knowledge, the MSA method has not been applied to the segmentation of renal cysts and parenchyma in ADPKD. Thus, the objective of our study was to demonstrate the feasibility of measuring the parenchyma and renal cyst volume using MSA methods. ADPKD phantoms were constructed and imaged. Clinical MR images from patients with ADPKD were acquired and analyzed. The measurements acquired with the MSA method were compared with the reference measurements which were obtained with the fluid displacement method for the phantom images, and obtained with the stereology and regionbased thresholding methods for the phantom and clinical images. 2. MATERIALS AND METHODS 2.1 Phantoms Phantoms of two different sizes (small and large) were constructed with agarose (simulating kidney parenchyma) and water filled balloons (simulating cysts). Kidney-shaped molds were designed and formed with plastic. Latex balloons were filled with varying amounts of water using a syringe and tied with thread to simulate cysts of 5-30 ml. The volume of the balloons was measured with a graduated cylinder using the fluid displacement method. While the agarose solution was poured into a mold, balloons were submerged in the solution. The molds was placed in a refrigerator to solidify the agarose to a hardened gel state (7, 22-24). The phantoms were solid at room temperature and were taken from the molds. The volumes of the phantoms were also measured using the fluid displacement method. 2.2 Subjects The Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study was implemented to acquire prospective, cross-sectional and multi-year longitudinal measurements of renal and renal cyst volumes in a large cohort of ADPKD subjects. The protocol for the CRISP study was approved by the institutional review board at each participating site, and informed consent was obtained from all the subjects. The CRISP study subjects were ADPKD subjects between 15 and 46 years old with relatively normal glomerular filtration rates (GFR) (i.e., a 24-hour creatinine clearance > 70 ml/min/1.73 m 2 or a Cockcroft-Gault estimate of creatinine clearance > 70 ml/min). Detailed descriptions of the CRISP study protocol, the clinical characteristics of the cohort and the baseline characteristics have been published (6, 8, 9, 25). For our study of developing and testing the MSA segmented method, three subjects were selected from the CRISP subject cohort. 2.3 MR Imaging Protocol Both phantoms and subjects were imaged using 1.5 T MRI scanners. The MRI protocol for the CRISP study was standardized and implemented (6, 8, 9, 25). In each subject, a phased-array surface coil was positioned with its center over the inferior costal margin, estimated as the upper margin of the kidney. The field of view was maintained between 30 and 35 cm. The kidneys were imaged first posteroanterior in the coronal plane using the T2-weighted single-shot fast spin-echo (SSFSE/HASTE) sequence with fat saturation at 3 mm fixed slice thickness during breath-hold(s). After the T2-weighted images were obtained, breath-hold coronal, three-dimensional spoiled gradient interpolated (SPGR/VIBE) T1-weighted images without fat saturation at 3 mm fixed-slice thickness were obtained. Following this acquisition, 0.1 Proc. of SPIE Vol

3 mmol/kg gadolinium was injected at 1 ml/sec. The T1-weighted imaging sequence was repeated 120 and 180 seconds after the start of the gadolinium injection. For MR imaging of phantoms, each phantom was placed and suspended in a vegetable-oil bath which simulates perinephric fat, and the oil bath was placed into a water bath on the MR table. A body phased-array surface coil was placed over the approximate center of the phantom. T2- and T1- weighted images of the phantoms were acquired using imaging parameters similar to the imaging of the subjects (Fig. 1). No gadolinium enhanced images was obtained for the phantoms. Figure 1. A coronal T1- and T2-weighted images of the polycystic kidney (A) small phantom and (B) large phantom consisting of agarose (kidney parenchyma) and water-filled balloons (cysts) formed in the shape of a kidney. The phantoms were immersed in an oil bath. The oil bath remains dark in MR signal intensity in this fatsaturation imaging sequence. 2.4 Image Co-registration and Multi-spectral Analysis Method For the MSA method, T1- and T2-weighted images of the kidneys should be co-registered. The co-registration process was readily conducted in the phantoms because the phantoms remained stationary against a homogenous vegetable oil background. Clinical images, however, were complex and difficult to co-register because of patient respiration and movement between T1 and T2 scans, and a heterogeneous anatomical background. In order to co-register the kidneys in the clinical images, three or four dominant renal cysts that were present in each kidney were identified between the T1 and T2 images by the operator. These cysts were used to three-dimensionally register the kidneys by surface matching. This pre-registration image processing was performed using Analyze software (Mayo Foundation, Biomedical Imaging Resource, Rochester, MN). After the T1- and T2-weighted images were registered, the MSA process was performed using Analyze software. In the MSA method, each pixel was displayed in a scatter plot at the location determined by the intensity of the pixel in the T1- and T2-weighted images. Then, coherent regions were classified by clustering pixels in the feature space. The MSA method was applied to a set of images to compute the volumes of renal parenchyma and cysts. Various representative tissue regions such as renal cysts, parenchyma, liver for right kidney and spleen for the left kidney, and fat were sampled within the chosen slice to determine their signal intensity locations on the 2D scatter gram (Fig. 2). A sufficient number of tissue classes were necessary to include all the tissue types visualized in the image. After each region in the slice was sampled and enclosed, the scatter gram was classified using a Gaussian classification method. Next, all of the slices in the file (volume) were classified using the classified scatterogram. The classified voxels of different tissue-regions were subsequently denoted and displayed in different colors (Fig. 2). Renal parenchyma and cyst volumes were measured by summing the number of voxels in corresponding tissue groups and converting them into the volume measurements. Figure 2. Coronal (A) T1- and (B) T2-weighted MR images of the left kidney of an ADPKD subject with (C, D) their voxel points displayed in the MSA T1/T2 feature space and (E) the final segmented kidney image. The voxels in red and yellow represent renal cysts, while the voxels in green represent renal parenchyma. The voxels from the fat, spleen, and muscle have characteristic signal intensity distribution in the (T1, T2) feature space and are denoted in colors distinct from the kidney parenchyma and cysts. Proc. of SPIE Vol

4 2.5 Renal Volume Measurement using Stereology Method The volume of each kidney was measured from the T1-weighted images using the stereology method. The stereology method has been widely used for image-based volumetric measurements of a variety of organs (7, 26-33), because it provides a fast and reliable measurement of the area of an object. Its measurement is based on counting the number of intersections of a randomly oriented and positioned grid over the object. In our application, the area of the kidney in each image was calculated by converting the sum of the selected grid points overlaid the kidney to a area pixel count (7, 8). The total renal volume was calculated from the set of contiguous images by summing the products of the area measurements and the slice thickness. 2.6 Renal Cyst Volume Measurement using Region-Based Thresholding Method Because of their water content, cysts demonstrate signal intensities higher than the background renal parenchyma in T2-weighted images. The renal cyst volume was measured from T2-weighted images using a region-based thresholding method (7, 8). First, the whole kidney was segmented using a region-growing method by placing a seed starting point over the kidney region in each image. A threshold was selected interactively by an analyst on the T2-weighted images. After segmentation of the whole kidney, an additional thresholding operation was performed on a slice by slice basis to segment the cysts from the parenchyma. A histogram plot of all voxel intensity values was used to set the display intensity for all slices. A threshold was visually determined and adjusted to extract the cysts in the image. Cysts were segmented from the voxels whose intensity values were greater than the threshold. This procedure was repeated in each slice of the kidney. Cyst areas were calculated in each image, and the total cyst volume was calculated from each set of contiguous images by summing the products of the area measurements and the slice thickness. 2.7 Data Analysis The renal volumes measured with the MSA method were compared to those made by the stereology method in the phantoms and subjects. The renal cyst volumes measured with the MSA method were compared to those made by the region-based thresholding methods in the phantoms and subjects. In addition, the renal and renal cyst volumes of the phantoms were compared with those by the fluid displacement method. The distributions of the differences in volumes with the different techniques were tested for normality with Shapiro-Wilk W tests. Differences in volumes were tested for statistical significance with paired t tests. Ninety-five percent confidence intervals were calculated for the differences. 3. RESULTS For the phantoms, the reference volumes were measured by the fluid displacement method. A total kidney (i.e., agarose plus water-filled balloons) volume of cc was measured for the small phantom and cc for the large phantom. A total cyst (i.e., water-filled balloons) volume of 68 cc was measured for the small phantom and 190 cc for the large phantom. The stereology and region-based volume measurements were slightly more accurate than those by the MSA method in both small and large phantoms (Table 1). For the PKD subjects, because the kidneys and cysts cannot be measured with the fluid displacement method, no reference measurement was available. The renal volume measurement of each kidney was compared between the MSA and stereology methods, while the renal cyst volume of each kidney was compared between the MSA and region-based methods (Table 2). The distributions of the differences in volumes with the different techniques were normally distributed (Shapiro-Wilk W test p values > 0.27). Right side renal volumes with the MSA and stereology methods differed by -76 cc (-278 cc to 126 cc, 95% confidence interval, p = 0.25.). The mean difference between the MSA and stereology measurements for the left side was -45 cc (-218 cc to 128 cc, p = 0.38). For cyst volumes, the mean difference between MSA based and region-based volumes for the right side was 7 cc (-31 cc to 44 cc, p = 0.52) and for the left side 3 cc (-75 cc to 80 cc, p = 0.90). While the success of stereology and region-based thresholding methods relies heavily on the performance of a welltrained operator conducting the segmentation process, the MSA method is semi-automated and less operator-dependent. The MSA method, however, requires accurate registration of T1- and T2-weighted images and a high signal intensity contrast between tissues to segment. We found that the image co-registration and tissue classification processes of the MSA method were time-consuming, particularly for the clinical MR images. The MSA method typically required 3-4 hours to segment and measure each kidney from the clinical MR images, whereas hours per kidney was typically taken for the segmentation with both stereology and region-based thresholding methods. Proc. of SPIE Vol

5 Table 1. The kidney (whole phantom) and cyst (water-filled balloon) volumes of two phantoms measured with different methods. Volume measurement method Small phantom (cc) Large phantom (cc) Kidney Cyst Kidney Cyst Reference fluid displacement MSA Stereology/region-based thresholding Table 2. Renal and renal cyst volumes measured with different methods in 3 ADPKD subjects. Measurement and methods Subject A Kidney (cc) Subject B Kidney (cc) Subject C Kidney (cc) Right Left Right Left Right Left Renal volume by MSA Renal volume by Stereology Cyst volume by MSA Cyst volume by Region-based DISCUSSION Autosomal polycystic kidney disease (ADPKD) is a common disorder, affecting a half million people in the USA alone and constituting the fourth leading cause of chronic renal failure. Imaging plays a major role in the screening and diagnosis as well as management of ADPKD (2). Imaging may also be used to document the progression of disease or monitor the response of treatment and intervention. With rapid technical developments in radiologic imaging, morphologic changes in the kidney, i.e. the hallmark of ADPKD, can be accurately quantified. The major purpose of this study is to develop a new segmentation and quantification method of accurately and reliably measuring cysts and renal parenchyma from MR images. MRI has been increasingly used for volume measurement because it provides high resolution 3D images with excellent tissue contrast and no need for ionizing radiation or iodinated contrast medium. Acquired MR images can be segmented to generate morphometric measures. MR images, however, intrinsically suffer from spatial intensity fluctuations, mainly caused by inhomogeneities in the RF coil sensitivity, shading artifacts, partial volume effect, and noise (11). Thus, MR image segmentation methods relying on absolute image intensity values usually fail to perform properly. Unless there are severe intensity variations, the human visual system overcomes minor spatial intensity fluctuations and is well suited for image interpretation. Although numerous segmentation techniques may be available (11, 12) that allow us to measure kidney and cyst volumes from MR images in patients with ADPKD, implementing automatic or semiautomatic segmentation methods that provide a high measurement accuracy and reliability is not trivial. Segmentation using simple intensity thresholding is based on the premise that the intensity values for a particular material such as water are constant within a region of interest (ROI). Thresholding also assumes the intensity level for an ROI is significantly different from that of surrounding regions. This premise is frequently not valid in MR images because of intrinsic spatial intensity fluctuations. With consideration of these challenges, our choice of segmentation methods should focus on obtaining reliable volumetric measurements with high efficiency. In this study, we developed and tested a MSA method for the segmentation and volumetric measurement of the kidney and renal cysts. MSA uses an intrinsic multispectral property of MR images and has been applied by many researchers to segment and measure anatomical structures or lesions of the brain and other organs (13-21). Multispectral images consist of voxels in a feature space whose coordinate values reflect measurements of different physical attributes, e.g., T1- and T2-signal intensities, of the same object. The promise of multispectral analysis is that the dimensionally expanded feature space will allow tissue differentiations and segmentation. In our approach, certain voxels are defined by the user as belonging to certain classes, and these voxels are used as samples of these classes. Unassigned voxels are then classified based on the estimated likelihood of their belonging to each of the defined classes. We found that renal volume measurements by stereology and MSA were comparable. The stereology method relies heavily on the operator s perception and segmentation in each image slice to measure renal volumes. Our incentive to develop the MSA method was to reduce the subjective manual process of stereology required for segmenting and Proc. of SPIE Vol

6 quantifying of renal volumes. We postulated that T1 and T2 signal intensities that are characteristic of different tissue types would facilitate the segmentation procedure. In practice, however, we encountered several limitations of applying the MSA method to our segmentation and quantification of renal and renal cyst volumes. MR images are inherently heterogeneous within and between successive slices. The heterogeneity in the image and between images was not constant between T1- and T2-weighted images. Therefore, the heterogeneity increased complexity in the segmentation process using MSA. When a structure is not homogeneous within and between the images, this structure may be classified as different tissue types depending on the image location. Coil placement and body size could also cause inhomogeneity problems in the MR images, particularly when the kidney is large and the coil does not cover the entire kidney or the coil is not placed properly. Inherent to the MSA method is the requirement to register at least two different images of the same structure. If the images are registered inaccurately, the area/volume measurement would be erroneous. Registration of T1- and T2- weighted clinical MR images is complicated because of breathing or any subject s motion between the two MR scan acquisitions. These motion artifacts or motion-related misregistration may cause the edges of the kidney/cysts to be blurred. Blurred edges may result in mis-classification of tissue types and quantification errors. Furthermore, compared to the brain, the kidney is less rigid and more deformable between MR scans, making the registration process more challenging. The phantom images were registered readily because there was no motion between the scans and because the background of the MR images was homogenous in structure and signal intensity. After the registration was performed, each pair of registered pixels was displayed in the (T1, T2) feature space and classified as a pre-determined tissue type. For accurate classification, tissues should have high signal intensity contrast and be homogenous within each tissue type in each image and between the images. When tissue homogeneity is not sustained, voxels of the same tissue type are widely distributed and may result in many classes. The large number of classes would require the operator to review each image to determine the appropriate classification of different tissue types, resulting in a time-consuming classification process. Whereas the phantom MR images were readily registered and segmented with the MSA method, the clinical MR images were subjected to patient motion blurring and complex tissue signal intensity variations which required time-consuming MSA registration and tissue classification processes. As a result, stereology and region-based thresholding methods that mainly rely on the human visual system were more efficient to overcome spatial intensity fluctuations and were better suited for the segmentation and quantification of the renal and renal cyst volumes from clinical MR images. In conclusion, we have developed a MSA method and tested its performance for the segmentation and quantification of the renal and renal cyst volumes in phantoms and patients. The MSA volume measurements were comparable to stereology and region-based thresholding methods. We, however, found that the premise of MSA method, i.e., taking advantage of tissue-specific signal-intensity characteristics, is difficult to implement in reality because of patient motion and intrinsic inhomogenous signal intensities in renal MR images. Co-registration and image pre-processing required in the MSA method was computationally demanding. In particular, the image qualities in the patient data were more variable, thereby making the MSA segmentation technique more challenging. 5. ACKNOWLEDGEMENTS The study was supported by cooperative agreements (DK56934, DK56956, DK56957 and DK56961) with the National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, and the general clinical research centers at the participating institutions. Analyze software was provided by Dr. Richard A. Robb of the Mayo Biomedical Imaging Resource in Rochester, MN. REFERENCES 1. Gabow PA. Autosomal dominant polycystic kidney disease. N Engl J Med 1993; 329: Choyke PL. Inherited cystic diseases of the kidney. Radiol Clin North Am 1996; 34: Choukroun G, Itakura Y, Albouze G, et al. Factors influencing progression of renal failure in autosomal dominant polycystic kidney disease. J Am Soc Nephrol 1995; 6: Proc. of SPIE Vol

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