Deep learning-based glomerulus detection and classification with generative morphology augmentation in renal pathology images.

Glomerulus morphology on renal pathology images provides valuable diagnosis and outcome prediction information. To provide better care, an efficient, standardized, and scalable method is urgently needed to optimize the time-consuming and labor-intensive interpretation process by renal pathologists. This paper proposes a deep convolutional neural network (CNN)-based approach to automatically detect and classify glomeruli with different stains in renal pathology images. In the glomerulus detection stage, this paper proposes a flattened Xception with a feature pyramid network (FX-FPN). The FX-FPN is employed as a backbone in the framework of faster region-based CNN to improve glomerulus detection performance. In the classification stage, this paper considers classifications of five glomerulus morphologies using a flattened Xception classifier. To endow the classifier with higher discriminability, this paper proposes a generative data augmentation approach for patch-based glomerulus morphology augmentation. New glomerulus patches of different morphologies are generated for data augmentation through the cycle-consistent generative adversarial network (CycleGAN). The single detection model shows the F1 score up to 0.9524 in H&E and PAS stains. The classification result shows that the average sensitivity and specificity are 0.7077 and 0.9316, respectively, by using the flattened Xception with the original training data. The sensitivity and specificity increase to 0.7623 and 0.9443, respectively, by using the generative data augmentation. Comparisons with different deep CNN models show the effectiveness and superiority of the proposed approach.

Global attention based GNN with Bayesian collaborative learning for glomerular lesion recognition.

BACKGROUND: Glomerular lesions reflect the onset and progression of renal disease. Pathological diagnoses are widely regarded as the definitive method for recognizing these lesions, as the deviations in histopathological structures closely correlate with impairments in renal function. METHODS: Deep learning plays a crucial role in streamlining the laborious, challenging, and subjective task of recognizing glomerular lesions by pathologists. However, the current methods treat pathology images as data in regular Euclidean space, limiting their ability to efficiently represent the complex local features and global connections. In response to this challenge, this paper proposes a graph neural network (GNN) that utilizes global attention pooling (GAP) to more effectively extract high-level semantic features from glomerular images. The model incorporates Bayesian collaborative learning (BCL), enhancing node feature fine-tuning and fusion during training. In addition, this paper adds a soft classification head to mitigate the semantic ambiguity associated with a purely hard classification. RESULTS: This paper conducted extensive experiments on four glomerular datasets, comprising a total of 491 whole slide images (WSIs) and 9030 images. The results demonstrate that the proposed model achieves impressive F1 scores of 81.37%, 90.12%, 87.72%, and 98.68% on four private datasets for glomerular lesion recognition. These scores surpass the performance of the other models used for comparison. Furthermore, this paper employed a publicly available BReAst Carcinoma Subtyping (BRACS) dataset with an 85.61% F1 score to further prove the superiority of the proposed model. CONCLUSION: The proposed model not only facilitates precise recognition of glomerular lesions but also serves as a potent tool for diagnosing kidney diseases effectively. Furthermore, the framework and training methodology of the GNN can be adeptly applied to address various pathology image classification challenges.

An Efficient and Fast Method for Labeling and Analyzing Mouse Glomeruli.

The glomeruli are fundamental units in the kidney; hence, studying the glomeruli is pivotal for understanding renal function and pathology. Biological imaging provides intuitive information; thus, it is of great significance to label and observe the glomeruli. However, the glomeruli observation methods currently in use require complicated operations, and the results may lose label details or three-dimensional (3D) information. The clear, unobstructed brain imaging cocktails and computational analysis (CUBIC) tissue clearing technology has been widely used in renal research, allowing for more accurate detection and deeper detection depth. We found that mouse glomeruli can be rapidly and effectively labeled by tail vein injection of medium molecular weight FITC-Dextran followed by the CUBIC clearing method. The cleared mouse kidney could be scanned by a light-sheet microscope (or a confocal microscope when sliced) to obtain three-dimensional image stacks of all the glomeruli in the entire kidney. Processed with appropriate software, the glomeruli signals could be easily digitized and further analyzed to measure the number, volume, and frequency of the glomeruli.

An image inpainting-based data augmentation method for improved sclerosed glomerular identification performance with the segmentation model EfficientNetB3-Unet.

The percent global glomerulosclerosis is a key factor in determining the outcome of renal transfer surgery. At present, the rate is typically computed by pathologists, which is labour intensive and nonstandardized. With the development of Deep Learning (DL), DL-based segmentation models can be used to better identify and segment normal and sclerosed glomeruli. Based on this, we can better quantify percent global glomerulosclerosis to reduce the discard rate of donor kidneys. We used 51 whole slide images (WSIs) from different institutions that are publicly available on the internet. However, the number of sclerosed glomeruli is much smaller than that of normal glomeruli in different WSIs, which can reduce the effectiveness of Deep Learning. For better sclerosed glomerular identification and segmentation performance, we modified and trained a GAN (generative adversarial network)-based image inpainting model to obtain more synthetic sclerosed glomeruli. Our proposed inpainting method achieved an average SSIM (Structural Similarity) of 0.8086 and an average PSNR (Peak Signal-to-Noise Ratio) of 22.8943 dB in the area of generated sclerosed glomeruli. We obtained sclerosed glomerular segmentation performance improvement by adding synthetic sclerosed glomerular images and achieved the best Dice of glomerular segmentation in different test sets based on the modified Unet model.

FastCellpose: A Fast and Accurate Deep-Learning Framework for Segmentation of All Glomeruli in Mouse Whole-Kidney Microscopic Optical Images.

Automated evaluation of all glomeruli throughout the whole kidney is essential for the comprehensive study of kidney function as well as understanding the mechanisms of kidney disease and development. The emerging large-volume microscopic optical imaging techniques allow for the acquisition of mouse whole-kidney 3D datasets at a high resolution. However, fast and accurate analysis of massive imaging data remains a challenge. Here, we propose a deep learning-based segmentation method called FastCellpose to efficiently segment all glomeruli in whole mouse kidneys. Our framework is based on Cellpose, with comprehensive optimization in network architecture and the mask reconstruction process. By means of visual and quantitative analysis, we demonstrate that FastCellpose can achieve superior segmentation performance compared to other state-of-the-art cellular segmentation methods, and the processing speed was 12-fold higher than before. Based on this high-performance framework, we quantitatively analyzed the development changes of mouse glomeruli from birth to maturity, which is promising in terms of providing new insights for research on kidney development and function.

Sensing ultrasound localization microscopy for the visualization of glomeruli in living rats and humans.

BACKGROUND: Estimation of glomerular function is necessary to diagnose kidney diseases. However, the study of glomeruli in the clinic is currently done indirectly through urine and blood tests. A recent imaging technique called Ultrasound Localization Microscopy (ULM) has appeared. It is based on the ability to record continuous movements of individual microbubbles in the bloodstream. Although ULM improved the resolution of vascular imaging up to tenfold, the imaging of the smallest vessels had yet to be reported. METHODS: We acquired ultrasound sequences from living humans and rats and then applied filters to divide the data set into slow-moving and fast-moving microbubbles. We performed a double tracking to highlight and characterize populations of microbubbles with singular behaviors. We decided to call this technique "sensing ULM" (sULM). We used post-mortem micro-CT for side-by-side confirmation in rats. FINDINGS: In this study, we report the observation of microbubbles flowing in the glomeruli in living humans and rats. We present a set of analysis tools to extract quantitative information from individual microbubbles, such as remanence time or normalized distance. INTERPRETATION: As glomeruli play a key role in kidney function, it would be possible that their observation yields a deeper understanding of the kidney. It could also be a tool to diagnose kidney diseases in patients. More generally, it will bring imaging capabilities closer to the functional units of organs, which is a key to understand most diseases, such as cancer, diabetes, or kidney failures. FUNDING: This study was funded by the European Research Council under the European Union Horizon H2020 program (ERC Consolidator grant agreement No 772786-ResolveStroke).

Data augmentation based on spatial deformations for histopathology: An evaluation in the context of glomeruli segmentation.

BACKGROUND AND OBJECTIVE: The effective application of deep learning to digital histopathology is hampered by the shortage of high-quality annotated images. In this paper we focus on the supervised segmentation of glomerular structures in patches of whole slide images of renal histopathological slides. Considering a U-Net model employed for segmentation, our goal is to evaluate the impact of augmenting training data with random spatial deformations. METHODS: The effective application of deep learning to digital histopathology is hampered by the shortage of high-quality annotated images. In this paper we focus on the supervised segmentation of glomerular structures in patches of whole slide images of renal histopathological slides. Considering a U-Net model employed for segmentation, our goal is to evaluate the impact of augmenting training data with random spatial deformations. RESULTS: We show that augmenting training data with spatially deformed images yields an improvement of up to 0.23 in average Dice score, with respect to training with no augmentation. We demonstrate that deformations with relatively strong distortions yield the best performance increase, while previous work only report the use of deformations with low distortions. The selected deformation models yield similar performance increase, provided that their parameters are properly adjusted. We provide bounds on the optimal parameter values, obtained through parameter sampling, which is achieved in a lower computational complexity with our single-parameter method. The paper is accompanied by a framework for evaluating the impact of random spatial deformations on the performance of any U-Net segmentation model. CONCLUSION: To our knowledge, this study is the first to evaluate the impact of random spatial deformations on the segmentation of histopathological images. Our study and framework provide tools to help practitioners and researchers to make a better usage of random spatial deformations when training deep models for segmentation.

Intravital imaging reveals glomerular capillary distension and endothelial and immune cell activation early in Alport syndrome.

Alport syndrome (AS) is a genetic disorder caused by mutations in type IV collagen that lead to defective glomerular basement membrane, glomerular filtration barrier (GFB) damage, and progressive chronic kidney disease. While the genetic basis of AS is well known, the molecular and cellular mechanistic details of disease pathogenesis have been elusive, hindering the development of mechanism-based therapies. Here, we performed intravital multiphoton imaging of the local kidney tissue microenvironment in a X-linked AS mouse model to directly visualize the major drivers of AS pathology. Severely distended glomerular capillaries and aneurysms were found accompanied by numerous microthrombi, increased glomerular endothelial surface layer (glycocalyx) and immune cell homing, GFB albumin leakage, glomerulosclerosis, and interstitial fibrosis by 5 months of age, with an intermediate phenotype at 2 months. Renal histology in mouse or patient tissues largely failed to detect capillary aberrations. Treatment of AS mice with hyaluronidase or the ACE inhibitor enalapril reduced the excess glomerular endothelial glycocalyx and blocked immune cell homing and GFB albumin leakage. This study identified central roles of glomerular mechanical forces and endothelial and immune cell activation early in AS, which could be therapeutically targeted to reduce mechanical strain and local tissue inflammation and improve kidney function.

Application of direct stochastic optical reconstruction microscopy (dSTORM) to the histological analysis of human glomerular disease.

Electron microscopy (EM) following immunofluorescence (IF) imaging is a vital tool for the diagnosis of human glomerular diseases, but the implementation of EM is limited to specialised institutions and it is not available in many countries. Recent progress in fluorescence microscopy now enables conventional widefield fluorescence microscopes to be adapted at modest cost to provide resolution below 50 nm in biological specimens. We show that stochastically switched single-molecule localisation microscopy can be applied to clinical histological sections stained with standard IF techniques and that such super-resolved IF may provide an alternative means to resolve ultrastructure to aid the diagnosis of kidney disease where EM is not available. We have implemented the direct stochastic optical reconstruction microscopy technique with human kidney biopsy frozen sections stained with clinically approved immunofluorescent probes for the basal laminae and immunoglobulin G deposits. Using cases of membranous glomerulonephritis, thin basement membrane lesion, and lupus nephritis, we compare this approach to clinical EM images and demonstrate enhanced imaging compared to conventional IF microscopy. With minor modifications in established IF protocols of clinical frozen renal biopsies, we believe the cost-effective adaptation of conventional widefield microscopes can be widely implemented to provide super-resolved image information to aid diagnosis of human glomerular disease.

Serial intravital imaging captures dynamic and functional endothelial remodeling with single-cell resolution.

Endothelial cells are important in the maintenance of healthy blood vessels and in the development of vascular diseases. However, the origin and dynamics of endothelial precursors and remodeling at the single-cell level have been difficult to study in vivo owing to technical limitations. Therefore, we aimed to develop a direct visual approach to track the fate and function of single endothelial cells over several days and weeks in the same vascular bed in vivo using multiphoton microscopy (MPM) of transgenic Cdh5-Confetti mice and the kidney glomerulus as a model. Individual cells of the vascular endothelial lineage were identified and tracked owing to their unique color combination, based on the random expression of cyan/green/yellow/red fluorescent proteins. Experimental hypertension, hyperglycemia, and laser-induced endothelial cell ablation rapidly increased the number of new glomerular endothelial cells that appeared in clusters of the same color, suggesting clonal cell remodeling by local precursors at the vascular pole. Furthermore, intravital MPM allowed the detection of distinct structural and functional alterations of proliferating endothelial cells. No circulating Cdh5-Confetti+ cells were found in the renal cortex. Moreover, the heart, lung, and kidneys showed more significant clonal endothelial cell expansion compared with the brain, pancreas, liver, and spleen. In summary, we have demonstrated that serial MPM of Cdh5-Confetti mice in vivo is a powerful technical advance to study endothelial remodeling and repair in the kidney and other organs under physiological and disease conditions.

Analysis of the three dimensional structure of the kidney glomerulus capillary network.

The capillary network of the kidney glomerulus filters small molecules from the blood. The glomerular 3D structure should help to understand its function, but it is poorly characterized. We therefore devised a new approach in which an automated tape collecting microtome (ATUM) was used to collect 0.5 µm thick serial sections from fixed mouse kidneys. The sections were imaged by scanning electron microscopy at ~ 50 nm/pixel resolution. With this approach, 12 glomeruli were reconstructed at an x-y-z resolution ~ 10 × higher than that of paraffin sections. We found a previously undescribed no-cross zone between afferent and efferent branches on the vascular pole side; connections here would allow blood to exit without being adequately filtered. The capillary diameters throughout the glomerulus appeared to correspond with the amount of blood flow within them. The shortest path (minimum number of branches to travel from afferent to efferent arterioles) is relatively independent of glomerular size and is present primarily on the vascular pole size. This suggests that new branches and longer paths form on the urinary pole side. Network analysis indicates that the glomerular network does not form by repetitive longitudinal splitting of capillaries. Thus the 3D structure of the glomerular capillary network provides useful information with which to understand glomerular function. Other tissue structures in the body may benefit from this new three dimensional approach.

Live Imaging of Monocyte Subsets in Immune Complex-Mediated Glomerulonephritis Reveals Distinct Phenotypes and Effector Functions.

BACKGROUND: Immune complexes within glomerular capillary walls cause crescentic GN (CrGN). Monocytes and macrophages are important in mediating CrGN, but little work has been done to phenotype the subpopulations involved and determine their respective contributions to glomerular inflammation. METHODS: Live glomerular imaging using confocal microscopy monitored intravascular monocyte subset behavior during nephrotoxic nephritis (NTN) in a novel WKY-hCD68-GFP monocyte/macrophage reporter rat strain. Flow cytometry and qPCR further analyzed ex vivo the glomerular leukocyte infiltrate during NTN. RESULTS: Non-classical monocytes surveyed the glomerular endothelium via lymphocyte function-associated antigen 1 (LFA-1) in the steady state. During NTN, non-classical monocytes were recruited first, but subsequent recruitment and retention of classical monocytes was associated with glomerular damage. Monocytes recruited to the glomerular vasculature did not undergo transendothelial migration. This finding suggests that inflammation in immune complex-mediated CrGN is predominantly intravascular, driven by dynamic interactions between intravascular blood monocytes and the endothelium. Glomerular endothelium and non-classical monocytes overexpressed a distinct chemokine axis, which may orchestrate inflammatory myeloid cell recruitment and expression of damage mediators. Reduced classical monocyte recruitment in Lewis rats during NTN confirmed a role for CD16 in mediating glomerular damage. CONCLUSIONS: Monocyte subsets with distinct phenotypes and effector functions may be important in driving inflammation in experimental CrGN resulting from immune complexes formed within the glomerular capillary wall. LFA-1-dependent endothelial surveillance by non-classical monocytes may detect immune complexes through CD16, orchestrating the inflammatory response through intravascular retention of classical monocytes, which results in glomerular damage and proteinuria.

Mapping vascular and glomerular pathology in a rabbit model of neonatal acute kidney injury using MRI.

Acute kidney injury (AKI) in premature neonates is common due to the administration of life-saving therapies. The impact of AKI on renal morphology and susceptibility to further renal damage is poorly understood. Recent advances in radiological imaging have allowed integration of soft tissue morphology in the intact organ, facilitating a more complete understanding of changes in tissue microstructure associated with pathology. Here, we applied magnetic resonance imaging (MRI) to detect both glomerular and vascular changes in a rabbit model of neonatal AKI, induced by indomethacin and gentamicin. Using combined spin-echo MRI and cationic ferritin enhanced gradient-echo MRI (CFE-MRI), we observed (a) an increased cortical arterial diameter in the AKI cohort compared to healthy controls, and (b) focal loss of vascular density and glomerular loss in a circumferential band ~1 mm from the cortical surface. This combined use of vascular and glomerular imaging may give insight into the etiology of AKI and its impact on renal health later in life.

A case of ANCA associated vasculitis in a patient presenting with chest pain.

Antineutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV) is a group of multisystem autoimmune small vessel diseases. We report here a case of a 68-year-old woman who initially presented with 29-day history of chest pain, malaise and anorexia. Cardiac problems were ruled out and she was considered to have pneumonia. Her symptoms persisted and blood tests showed renal impairment and evidence of an inflammatory response. A kidney biopsy, chest computed tomography (CT) scan and ANCA testing confirmed a diagnosis of AAV renal injury. She was treated with glucocorticoids and cyclophosphamide (CTX) for six months at which time her kidney function had improved and she avoided the need for dialysis. This case study illustrates that the clinical manifestations of AVV are complex, varied, and prone to misdiagnosis.

Weight-gain induced changes in renal perfusion assessed by contrast-enhanced ultrasound precede increases in urinary protein excretion suggestive of glomerular and tubular injury and normalize after weight-loss in dogs.

Early detection of obesity-related glomerulopathy in humans is challenging as it might not be detected by routine biomarkers of kidney function. This study's aim was to use novel kidney biomarkers and contrast-enhanced ultrasound (CEUS) to evaluate the effect of obesity development and weight-loss on kidney function, perfusion, and injury in dogs. Sixteen healthy lean adult beagles were assigned randomly but age-matched to a control group (CG) (n = 8) fed to maintain a lean body weight (BW) for 83 weeks; or to a weight-change group (WCG) (n = 8) fed the same diet to induce obesity (week 0-47), to maintain stable obese weight (week 47-56) and to lose BW (week 56-83). At 8 time points, values of systolic blood pressure (sBP); serum creatinine (sCr); blood urea nitrogen (BUN); serum cystatin C (sCysC); urine protein-to-creatinine ratio (UPC); and urinary biomarkers of glomerular and tubular injury were measured. Glomerular filtration rate (GFR) and renal perfusion using CEUS were assayed (except for week 68). For CEUS, intensity- and time-related parameters representing blood volume and velocity were derived from imaging data, respectively. At 12-22% weight-gain, cortical time-to-peak, representing blood velocity, was shorter in the WCG vs. the CG. After 37% weight-gain, sCysC, UPC, glomerular and tubular biomarkers of injury, urinary immunoglobulin G and urinary neutrophil gelatinase-associated lipocalin, respectively, were higher in the WCG. sBP, sCr, BUN and GFR were not significantly different. After 23% weight-loss, all alterations were attenuated. Early weight-gain in dogs induced renal perfusion changes measured with CEUS, without hyperfiltration, preceding increased urinary protein excretion with potential glomerular and tubular injury. The combined use of routine biomarkers of kidney function, CEUS and site-specific urinary biomarkers might be valuable in assessing kidney health of individuals at risk for obesity-related glomerulopathy in a non-invasive manner.

Classification of glomerular hypercellularity using convolutional features and support vector machine.

Glomeruli are histological structures of the kidney cortex formed by interwoven blood capillaries, and are responsible for blood filtration. Glomerular lesions impair kidney filtration capability, leading to protein loss and metabolic waste retention. An example of lesion is the glomerular hypercellularity, which is characterized by an increase in the number of cell nuclei in different areas of the glomeruli. Glomerular hypercellularity is a frequent lesion present in different kidney diseases. Automatic detection of glomerular hypercellularity would accelerate the screening of scanned histological slides for the lesion, enhancing clinical diagnosis. Having this in mind, we propose a new approach for classification of hypercellularity in human kidney images. Our proposed method introduces a novel architecture of a convolutional neural network (CNN) along with a support vector machine, achieving near perfect average results on FIOCRUZ data set in a binary classification (lesion or normal). Additionally, classification of hypercellularity sub-lesions was also evaluated, considering mesangial, endocapilar and both lesions, reaching an average accuracy of 82%. Either in binary task or in the multi-classification one, our proposed method outperformed Xception, ResNet50 and InceptionV3 networks, as well as a traditional handcrafted-based method. To the best of our knowledge, this is the first study on deep learning over a data set of glomerular hypercellularity images of human kidney.

Unobstructed Multiscale Imaging of Tissue Sections for Ultrastructural Pathology Analysis by Backscattered Electron Scanning Microscopy.

Ultrastructural analysis of healthy, diseased, or experimental tissues is essential in diagnostic and investigative pathology. Evaluation of large tissue areas with suborganelle resolution is challenging because biological structures ranging from several millimeters to nanometers in size need to be identified and imaged while maintaining context over multiple scales. Imaging with field emission scanning electron microscopes (FE-SEMs) is uniquely suited for this task. We describe an efficient workflow for the preparation and unobstructed multiscale imaging of tissue sections with backscattered electron scanning electron microscopy (BSE-SEM) for applications in ultrastructural pathology. We demonstrate that a diverse range of tissues, processed by conventional electron microscopy protocols and avoiding the use of mordanting agents, can be imaged on standard glass slides over multiple scales, from the histological to the ultrastructural level, without any visual obstructions. Our workflow takes advantage of the very large scan fields possible with modern FE-SEMs that allow for the acquisition of wide-field overview images which can be explored at the ultrastructural level by digitally zooming into the images. Examples from applications in pulmonary research and neuropathology demonstrate the versatility and efficiency of this method. This BSE-SEM-based multiscale imaging procedure promises to substantially simplify and accelerate ultrastructural tissue analysis in pathology.

Histological Examination of the Diabetic Kidney.

The increasing prevalence of diabetes worldwide has led to a concomitant rise in diabetic kidney disease (DKD) as a major cause of end-stage renal disease. Glomerular lesions constitute the most striking and consistent features identified in biopsies from patients with DKD, although tubulointerstitial injury has an important and often under-recognized role in the progression to overt nephropathy. In advanced stages of the disease, podocyte detachment is a pivotal event in the loss of glomerular filtration barrier integrity and may explain, at least in part, the inability of current therapies to halt renal function decline. This chapter details the systematic method that can be used to study renal tissue samples from diabetic patients, and the specific role of different imaging techniques, such as light microscopy, immunofluorescence microscopy, and transmission and scanning electron microscopy in detecting histologic lesions specific to DKD.

Lupus Podocytopathy: An Overview.

In systemic lupus erythematosus, nephrotic-range proteinuria typically signals the presence of a proliferative lupus nephritis (class III/IV) and/or membranous lupus nephritis (class V, with or without concomitant class III or IV lesions). However, in rare instances, systemic lupus erythematosus patients with nephrotic syndrome have kidney biopsy findings of normal glomeruli or focal segmental glomerulosclerosis lesions, with or without mesangial proliferation, on light microscopy; the absence of subepithelial or subendothelial deposits on immunofluorescence and electron microscopy; and diffuse foot process effacement on electron microscopy. This pattern, termed lupus podocytopathy, is a unique form of lupus nephritis that mimics minimal change disease or primary focal segmental glomerulosclerosis and represents approximately 1% of lupus nephritis biopsies. Here we review the clinical features, histological manifestations, diagnostic criteria and classification, pathogenesis, treatment, and prognosis of lupus podocytopathy.

In vivo measurements of kidney glomerular number and size in healthy and Os/+ mice using MRI.

The development of chronic kidney disease (CKD) is associated with the loss of functional nephrons. However, there are no methods to directly measure nephron number in living subjects. Thus, there are no methods to track the early stages of progressive CKD before changes in total renal function. In this work, we used cationic ferritin-enhanced magnetic resonance imaging (CFE-MRI) to enable measurements of glomerular number (Nglom) and apparent glomerular volume (aVglom) in vivo in healthy wild-type (WT) mice (n = 4) and mice with oligosyndactylism (Os/+; n = 4), a model of congenital renal hypoplasia leading to nephron reduction. We validated in vivo measurements of Nglom and aVglom by high-resolution ex vivo MRI. CFE-MRI measured a mean Nglom of 12,220 ± 2,028 and 6,848 ± 1,676 (means ± SD) for WT and Os/+ mouse kidneys in vivo, respectively. Nglom measured in all mice in vivo using CFE-MRI varied by an average 15% from Nglom measured ex vivo in the same kidney (α = 0.05, P = 0.67). To confirm that CFE-MRI can also be used to track nephron endowment longitudinally, a WT mouse was imaged three times by CFE-MRI over 2 wk. Values of Nglom measured in vivo in the same kidney varied within ~3%. Values of aVglom calculated from CFE-MRI in vivo were significantly different (~15% on average, P < 0.01) from those measured ex vivo, warranting further investigation. This is the first report of direct measurements of Nglom and aVglom in healthy and diseased mice in vivo.