학술논문

Kidney Segmentation in Renal Magnetic Resonance Imaging - Current Status and Prospects
Document Type
Periodical
Source
IEEE Access Access, IEEE. 9:71577-71605 2021
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Image segmentation
Kidney
Magnetic resonance imaging
Diseases
Manuals
Deep learning
Biomarkers
Renal MRI
image segmentation
deep learning
Language
ISSN
2169-3536
Abstract
Magnetic resonance imaging has achieved an increasingly important role in the clinical work-up of renal diseases such chronic kidney disease (CKD). A large panel of parameters have been proposed to diagnose CKD among them total kidney volume (TKV) which recently qualified as biomarker. Volume estimation in renal MRI is based on image segmentation of the kidney and/or its compartments. Beyond volume estimation renal segmentation supports also the quantification of other MR based parameters such as perfusion or filtration. The aim of the present article is to discuss the recent existing literature on renal image segmentation techniques and show today’s limitations of the proposed techniques that might hinder clinical translation. We also provide pointers to open source software related to renal image segmentation.