Renal structural image processing techniques: a systematic review

Abstract

Background and objective: Renal disease, such as nephritis and nephropathy, is very harmful to human health. Accordingly, how to achieve early diagnosis and enhance treatment for kidney disorders would be the important lesion. Nevertheless, the clues from the clinical data, such as biochemistry examination, serological examination, and radiological studies are quite indirect and limited. It is no doubt that pathological examination of kidney will supply the direct evidence. There is a requirement for greater understanding of image processing techniques for renal diagnosis to optimize treatment and patient care.Methods: This study aims to systematically review the literature on publications that has been used image processing methods on pathological microscopic image for renal diagnosis.Results: Nine included studies revealed image analysis techniques for the diagnosis of renal abnormalities on pathological microscopic image, renal image studies are clustered as follows: Glomeruli Segmentation and analysis of the Glomerular basement membrane (55/55%), Blood vessels and tubules classification and detection (22/22%) and The Grading of renal cell carcinomas (22/22%).Conclusions: A medical image analysis method should provide an auto-adaptive and no external-human action dependency. In addition, since medical systems should have special characteristics such as high accuracy and reliability then clinical validation is highly recommended. New high-quality studies based on Moore neighborhood contour tracking method for glomeruli segmentation and using powerful texture analysis techniques such as the local binary pattern are recommended.

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

“…Nearly 50% of patients are found to be advanced. [ 16 , 17 ] Clear cell carcinoma of the kidney is usually asymptomatic in its early stages or only presents with systemic symptoms such as fever and fatigue, and tumor size increases. Renal clear cell carcinoma has the highest mortality among all urinary system tumors, and lymphatic system metastasis is one of the main reasons for poor clinical treatment.…”

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

“…The bulk of research in the digital pathology of kidneys that involves the segmentation of blood vessels addresses the problem of non-tumor diseases. It is important to detect blood vessels, distinguish elements such as the endothelium, measure wall thickness and hyalinosis level, and determine the presence or absence of inflammatory cells in a range of kidney pathologies [ 26 , 41 , 42 ]. These features are strictly necessary in order to perform a diagnosis of acute or chronic transplant rejection, some glomerulo- and vasculopathies, and level of kidney failure in patients with long-term primary of symptomatic hypertension ( Figure 3 and Figure 4 ).…”

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

“…Progressive sclerosis of glomeruli is also a universal feature of any chronic kidney disease. However, current clinical imaging techniques cannot visualize individual glomeruli 6–9 . The significant challenge lies in the diameter of human glomeruli, which is approximately 200 μm 10 and falls below the resolution limit of most medical imaging methods.…”