Kidney Segmentation in Renal Magnetic Resonance Imaging-Current Status and Prospects

被引：27

作者：

Zoellner, Frank G. ^{[1
]}

Kocinski, Marek ^{[2
,4
]}

Hansen, Laura ^{[1
]}

Golla, Alena-Kathrin ^{[1
]}

Trbalic, Amira Serifovic ^{[3
]}

Lundervold, Arvid ^{[2
]}

Materka, Andrzej ^{[4
]}

Rogelj, Peter ^{[5
]}

机构：

[1] Heidleberg Univ, Mannheim Inst Intelligent Syst Med Comp Assisted, Med Fac Mannheim, D-68167 Mannheim, Germany

[2] Univ Bergen, Mohn Med Imaging & Visualizat Ctr, Dep Biomed, NO-5020 Bergen, Norway

[3] Univ Tuzla, Fac Elect Engn, Tuzla 75000, Bosnia & Herceg

[4] Lodz Univ Technol, Inst Elect, PL-90924 Lodz, Poland

[5] Univ Primorska, Fac Math Nat Sci & Informat Technol, Koper 6000, Slovenia

来源：

IEEE ACCESS | 2021年 / 9卷

关键词：

Image segmentation; Kidney; Magnetic resonance imaging; Diseases; Manuals; Deep learning; Biomarkers; Renal MRI; image segmentation; deep learning; AUTOMATED SEGMENTATION; SEMIAUTOMATIC SEGMENTATION; TUMOR SEGMENTATION; POLYCYSTIC KIDNEYS; MR-IMAGES; DISEASE; VOLUME; SYSTEM; ACQUISITION; EXTRACTION;

D O I：

10.1109/ACCESS.2021.3078430

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：71577 / 71605

页数：29

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