Renal structural image processing techniques: a systematic review

Visualization of Renal Glomeruli in Human Native Kidneys With Sensing Ultrasound Localization Microscopy

OBJECTIVES

Kidney diseases significantly impact individuals' quality of life and strongly reduce life expectancy. Glomeruli play a crucial role in kidney function. Current imaging techniques cannot visualize them due to their small size. Sensing ultrasound localization microscopy (sULM) has shown promising results for visualizing in vivo the glomeruli of human kidney grafts. This study aimed to evaluate the ability of sULM to visualize glomeruli in vivo in native human kidneys despite their depth and a shorter duration of ultrasound acquisition limited by the period of the patient's apnea. Sensing ultrasound localization microscopy parameters in native kidneys and kidney grafts and their consequence regarding glomeruli detection were also compared.

MATERIALS AND METHODS

Exploration by sULM was conducted in 15 patients with native kidneys and 5 with kidney allografts. Glomeruli were counted using a normalized distance metric projected onto sULM density maps. The difference in the acquisition time, the kidney depth, and the frame rate between native kidneys and kidney grafts and their consequence regarding glomeruli detection were assessed.

RESULTS

Glomerular visualization was achieved in 12 of 15 patients with native kidneys. It failed due to impossible breath-holding for 2 patients and a too-deep kidney for 1 patient. Sensing ultrasound localization microscopy found 16 glomeruli per square centimeter in the native kidneys (6-31) and 33 glomeruli per square centimeter in kidney transplant patients (18-55).

CONCLUSIONS

This study demonstrated that sULM can visualize glomeruli in native human kidneys in vivo. The proposed method may have many hypothetical applications, including biomarker development, assisting biopsy, or potentially avoiding it. It establishes a framework for improving the detection of local microstructural pathology, influencing the evaluation of allografts, and facilitating disease monitoring in the native kidney.

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.

Advances in imaging techniques to assess kidney fibrosis

As a sign of chronic kidney disease (CKD) progression, renal fibrosis is an irreversible and alarming pathological change. The accurate diagnosis of renal fibrosis depends on the widely used renal biopsy, but this diagnostic modality is invasive and can easily lead to sampling error. With the development of imaging techniques, an increasing number of noninvasive imaging techniques, such as multipara meter magnetic resonance imaging (MRI) and ultrasound elastography, have gained attention in assessing kidney fibrosis. Depending on their ability to detect changes in tissue stiffness and diffusion of water molecules, ultrasound elastography and some MRI techniques can indirectly assess the degree of fibrosis. The worsening of renal tissue oxygenation and perfusion measured by blood oxygenation level-dependent MRI and arterial spin labeling MRI separately is also an indirect reflection of renal fibrosis. Objective and quantitative indices of fibrosis may be available in the future by using novel techniques, such as photoacoustic imaging and fluorescence microscopy. However, these imaging techniques are susceptible to interference or may not be convenient. Due to the lack of sufficient specificity and sensitivity, these imaging techniques are neither widely accepted nor proposed by clinicians. These obstructions must be overcome by conducting technology research and more prospective studies. In this review, we emphasize the recent advancement of these noninvasive imaging techniques and provide clinicians a continuously updated perspective on the assessment of kidney fibrosis.

Artificial Intelligence Assists in the Detection of Blood Vessels in Whole Slide Images: Practical Benefits for Oncological Pathology

The analysis of the microvasculature and the assessment of angiogenesis have significant prognostic value in various diseases, including cancer. The search for invasion into the blood and lymphatic vessels and the assessment of angiogenesis are important aspects of oncological diagnosis. These features determine the prognosis and aggressiveness of the tumor. Traditional manual evaluation methods are time consuming and subject to inter-observer variability. Blood vessel detection is a perfect task for artificial intelligence, which is capable of rapid analyzing thousands of tissue structures in whole slide images. The development of computer vision solutions requires the segmentation of tissue regions, the extraction of features and the training of machine learning models. In this review, we focus on the methodologies employed by researchers to identify blood vessels and vascular invasion across a range of tumor localizations, including breast, lung, colon, brain, renal, pancreatic, gastric and oral cavity cancers. Contemporary models herald a new era of computational pathology in morphological diagnostics.

Higher TYROBP and lower SOX6 as predictive biomarkers for poor prognosis of clear cell renal cell carcinoma: A pilot study

BACKGROUND

Clear cell renal carcinoma (ccRCC) is the most common subtype of renal cancer, accounting for approximately 75% of all histological types of renal cancer, and is the leading cause of death from renal cancer. However, the molecular mechanism of tyrosine kinase binding protein (TYROBP) and sex-determining region Y Box-6 (SOX6) in the ccRCC was not precise.

METHODS

Bioinformatics analysis was performed to explore the hub role of TYROBP and SOX6 on the ccRCC. A total of 6 patients with clear cell renal cell carcinoma (ccRCC) were recruited. HE staining was performed to observe the pathology result of ccRCC. Immunohistochemistry and Immunofluorescence assay was made to detect the protein expression of TYROBP. Total RNA was extracted using TRIzol to examine the mRNA expression of TYROBP via the Real time quantitative polymerase chain reaction. The strong correlation between the expression of TYROBP and the survival time of ccRCC patients was performed by the BP neural network and support vector machine.

RESULTS

Compared with the control group, the expression of SOX6 was downregulated in the samples with ccRCC. However, the expression of TYROBP was higher in the samples with ccRCC than in the control group. Compared with the patients with high SOX6 expression, the patients with low SOX6 expression have a poor survival prognosis (HR=0.39, P < .05). However, the patients with high TYROBP expression have a shorter survival time than the patients with low TYROBP expression (HR=1.66, P < .05). The genes related with TYROBP and SOX6 are mainly enriched in the regulation of cell activation, leukocyte activation, negative regulation of cell activation, myeloid leukocyte activation, positive regulation of response to external stimulus, immune response-regulating signaling pathway. The interaction between TYROBP, SOX6, and kidney neoplasms was drawn, and the inference score of TYROBP and SOX6 on the kidney neoplasms was high.

CONCLUSION

In conclusion, TYROBP is highly expressed in renal clear cell carcinoma, and when this molecule is highly expressed, the survival prognosis of renal carcinoma is poor. TYROBP and SOX6 may be potential targets for diagnosing and treating renal clear cell carcinoma.

A Hybrid Random Forest Classifier for Chronic Kidney Disease Prediction from 2D Ultrasound Kidney Images

Chronic kidney disease (CKD) is one of the causes of mortality in almost all countries across the globe and the notable thing is its asymptomatic nature in the early stages. This disease is characterized by the gradual loss of kidney function in an individual. Frequently chronic kidney disease is diagnosed based on the Estimated Glomerular Filtration Rate (eGFR) determined from blood and urine tests. In order to reduce the risk factors arising due to chronic kidney disease, it is essential to be diagnosed in the earlier stages itself. This work proposes an automated chronic kidney disease detection based on the textural features of the kidney using a hybrid random forest classifier from 2D ultrasound kidney images. The proposed classifier is compared with the other competing machine learning classifiers through experimenting on a dataset of 150 images and gives a better accuracy of [Formula: see text] with [Formula: see text] of recall and precision, thus proving it to be promising in detecting CKD noninvasively in the early stages.