Deep learning segmentation of glomeruli on kidney donor frozen sections

Application of cloud server-based machine learning for assisting pathological structure recognition in IgA nephropathy

BACKGROUND

Machine learning (ML) models can help assisting diagnosis by rapidly localising and classifying regions of interest (ROIs) within whole slide images (WSIs). Effective ML models for clinical decision support require a substantial dataset of 'real' data, and in reality, it should be robust, user-friendly and universally applicable.

METHODS

WSIs of primary IgAN were collected and annotated. The H-AI-L algorithm which could facilitate direct WSI viewing and potential ROI detection for clinicians was built on the cloud server of matpool, a shared internet-based service platform. Model performance was evaluated using F1-score, precision, recall and Matthew's correlation coefficient (MCC).

RESULTS

The F1-score of glomerular localisation in WSIs was 0.85 and 0.89 for the initial and pretrained models, respectively, with corresponding recall values of 0.79 and 0.83, and precision scores of 0.92 and 0.97. Dichotomous differentiation between global sclerotic (GS) and other glomeruli revealed F1-scores of 0.70 and 0.91, and MCC values of 0.55 and 0.87, for the initial and pretrained models, respectively. The overall F1-score of multiclassification was 0.81 for the pretrained models. The total glomerular recall rate was 0.96, with F1-scores of 0.68, 0.56 and 0.26 for GS, segmental glomerulosclerosis and crescent (C), respectively. Interstitial fibrosis/tubular atrophy lesion similarity between the true label and model predictions was 0.75.

CONCLUSIONS

Our results underscore the efficacy of the ML integration algorithm in segmenting ROIs in IgAN WSIs, and the internet-based model deployment is in favour of widespread adoption and utilisation across multiple centres and increased volumes of WSIs.

A Spatially Guided Machine-Learning Method to Classify and Quantify Glomerular Patterns of Injury in Histology Images

Introduction The diagnosis of glomerular diseases is primarily based on visual assessment of histologic patterns. Semi-quantitative scoring of active and chronic lesions is often required to assess individual characteristics of the disease. Reproducibility of the visual scoring systems remains debatable, while digital and machine-learning technologies present opportunities to detect, classify and quantify glomerular lesions, also considering their inter- and intraglomerular heterogeneity.

MATERIALS AND METHODS

We performed a cross-validated comparison of three modifications of a convolutional neural network (CNN)-based approach for recognition and intraglomerular quantification of nine main glomerular patterns of injury. Reference values provided by two nephropathologists were used for validation. For each glomerular image, visual attention heatmaps were generated with a probability of class attribution for further intraglomerular quantification. The quality of classifier-produced heatmaps was evaluated by intersection over union metrics (IoU) between predicted and ground truth localization heatmaps.

RESULTS

A proposed spatially guided modification of the CNN classifier achieved the highest glomerular pattern classification accuracies, with area under curve (AUC) values up to 0.981. With regards to heatmap overlap area and intraglomerular pattern quantification, the spatially guided classifier achieved a significantly higher generalized mean IoU value compared to single-multiclass and multiple-binary classifiers.

CONCLUSIONS

We propose a spatially guided CNN classifier that in our experiments reveals the potential to achieve high accuracy for the localization of intraglomerular patterns.

Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet Model

Glomeruli are interconnected capillaries in the renal cortex that are responsible for blood filtration. Damage to these glomeruli often signifies the presence of kidney disorders like glomerulonephritis and glomerulosclerosis, which can ultimately lead to chronic kidney disease and kidney failure. The timely detection of such conditions is essential for effective treatment. This paper proposes a modified UNet model to accurately detect glomeruli in whole-slide images of kidney tissue. The UNet model was modified by changing the number of filters and feature map dimensions from the first to the last layer to enhance the model's capacity for feature extraction. Moreover, the depth of the UNet model was also improved by adding one more convolution block to both the encoder and decoder sections. The dataset used in the study comprised 20 large whole-side images. Due to their large size, the images were cropped into 512 × 512-pixel patches, resulting in a dataset comprising 50,486 images. The proposed model performed well, with 95.7% accuracy, 97.2% precision, 96.4% recall, and 96.7% F1-score. These results demonstrate the proposed model's superior performance compared to the original UNet model, the UNet model with EfficientNetb3, and the current state-of-the-art. Based on these experimental findings, it has been determined that the proposed model accurately identifies glomeruli in extracted kidney patches.

Tubular and Glomerular Size by Cortex Depth as Predictor of Progressive CKD after Radical Nephrectomy for Tumor

SIGNIFICANCE STATEMENT

Glomerular size differs by cortex depth. Larger nephrons are prognostic of progressive kidney disease, but it is unknown whether this risk differs by cortex depth or by glomeruli versus proximal or distal tubule size. We studied the average minor axis diameter in oval proximal and distal tubules separately and by cortex depth in patients who had radical nephrectomy to remove a tumor from 2019 to 2020. In adjusted analyses, larger glomerular volume in the middle and deep cortex predicted progressive kidney disease. Wider proximal tubular diameter did not predict progressive kidney disease independent of glomerular volume. Wider distal tubular diameter showed a gradient of strength of prediction of progressive kidney disease in the more superficial cortex than in the deep cortex.

BACKGROUND

Larger nephrons are prognostic of progressive kidney disease, but whether this risk differs by nephron segments or by depth in the cortex is unclear.

METHODS

We studied patients who underwent radical nephrectomy for a tumor between 2000 and 2019. Large wedge kidney sections were scanned into digital images. We estimated the diameters of proximal and distal tubules by the minor axis of oval tubular profiles and estimated glomerular volume with the Weibel-Gomez stereological model. Analyses were performed separately in the superficial, middle, and deep cortex. Cox proportional hazard models assessed the risk of progressive CKD (dialysis, kidney transplantation, sustained eGFR <10 ml/min per 1.73 m 2 , or a sustained 40% decline from the postnephrectomy baseline eGFR) with glomerular volume or tubule diameters. At each cortical depth, models were unadjusted, adjusted for glomerular volume or tubular diameter, and further adjusted for clinical characteristics (age, sex, body mass index, hypertension, diabetes, postnephrectomy baseline eGFR, and proteinuria).

RESULTS

Among 1367 patients were 62 progressive CKD events during a median follow-up of 4.5 years. Glomerular volume predicted CKD outcomes at all depths, but only in the middle and deep cortex after adjusted analyses. Proximal tubular diameter also predicted progressive CKD at any depth but not after adjusted analyses. Distal tubular diameter showed a gradient of more strongly predicting progressive CKD in the superficial than deep cortex, even in adjusted analysis.

CONCLUSIONS

Larger glomeruli are independent predictors of progressive CKD in the deeper cortex, whereas in the superficial cortex, wider distal tubular diameters are an independent predictor of progressive CKD.

Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects

Digital imaging and advanced microscopy play a pivotal role in the diagnosis of kidney diseases. In recent years, great achievements have been made in digital imaging, providing novel approaches for precise quantitative assessments of nephropathology and relieving burdens of renal pathologists. Developing novel methods of artificial intelligence (AI)-assisted technology through multidisciplinary interaction among computer engineers, renal specialists, and nephropathologists could prove beneficial for renal pathology diagnoses. An increasing number of publications has demonstrated the rapid growth of AI-based technology in nephrology. In this review, we offer an overview of AI-assisted renal pathology, including AI concepts and the workflow of processing digital image data, focusing on the impressive advances of AI application in disease-specific backgrounds. In particular, this review describes the applied computer vision algorithms for the segmentation of kidney structures, diagnosis of specific pathological changes, and prognosis prediction based on images. Lastly, we discuss challenges and prospects to provide an objective view of this topic.