Recent advances in medical image processing for the evaluation of chronic kidney disease - Alnazer, Israa; Bourdon, Pascal; Urruty, Thierry; Falou, Omar; Khalil, Mohamad; Shahin, Ahmad; Fernandez-Maloigne, Christine (Medical image analysis, 2021)
•We propose a survey covering qualitative and quantitative analysis applied on novel medical imaging techniques to evaluate renal function.•First, we summarize the use of different imaging modalities (MRI, UE, CT, PET and SPECT) in CKD diagnosis.•We show the power of AI to guide renal dysfunction diagnosis and prognosis.•The application of textural analysis as well as machine learning on medical images to predict renal disease is discussed.•The role of deep learning in automatic renal segmentation from medical images is summarized. Assessment of renal function and structure accurately remains essential in the diagnosis and prognosis of Chronic Kidney Disease (CKD). Advanced imaging, including Magnetic Resonance Imaging (MRI), Ultrasound Elastography (UE), Computed Tomography (CT) and scintigraphy (PET, SPECT) offers the opportunity to non-invasively retrieve structural, functional and molecular information that could detect changes in renal tissue properties and functionality. Currently, the ability of artificial intelligence to turn conventional medical imaging into a full-automated diagnostic tool is widely investigated. In addition to the qualitative analysis performed on renal medical imaging, texture analysis was integrated with machine learning techniques as a quantification of renal tissue heterogeneity, providing a promising complementary tool in renal function decline prediction. Interestingly, deep learning holds the ability to be a novel approach of renal function diagnosis. This paper proposes a survey that covers both qualitative and quantitative analysis applied to novel medical imaging techniques to monitor the decline of renal function. First, we summarize the use of different medical imaging modalities to monitor CKD and then, we show the ability of Artificial Intelligence (AI) to guide renal function evaluation from segmentation to disease prediction, discussing how texture analysis and machine learning techniques have emerged in recent clinical researches in order to improve renal dysfunction monitoring and prediction. The paper gives a summary about the role of AI in renal segmentation. [Display omitted]