Kidney Segmentation in Renal Magnetic Resonance Imaging - Current Status and Prospects
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 estima...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9427136/ |
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| author | Frank G. Zollner Marek Kocinski Laura Hansen Alena-Kathrin Golla Amira Serifovic Trbalic Arvid Lundervold Andrzej Materka Peter Rogelj |
| author_facet | Frank G. Zollner Marek Kocinski Laura Hansen Alena-Kathrin Golla Amira Serifovic Trbalic Arvid Lundervold Andrzej Materka Peter Rogelj |
| author_sort | Frank G. Zollner |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dcd6c2e5782b4c23921fe89b0bb34d63 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-dcd6c2e5782b4c23921fe89b0bb34d632025-01-16T00:01:05ZengIEEEIEEE Access2169-35362021-01-019715777160510.1109/ACCESS.2021.30784309427136Kidney Segmentation in Renal Magnetic Resonance Imaging - Current Status and ProspectsFrank G. Zollner0https://orcid.org/0000-0003-3405-1394Marek Kocinski1https://orcid.org/0000-0001-7088-4823Laura Hansen2Alena-Kathrin Golla3https://orcid.org/0000-0002-2506-4583Amira Serifovic Trbalic4https://orcid.org/0000-0003-4892-5945Arvid Lundervold5Andrzej Materka6https://orcid.org/0000-0003-0864-1518Peter Rogelj7https://orcid.org/0000-0003-2939-6945Medical Faculty Mannheim, Mannheim Institute for Intelligent Systems in Medicine, Computer Assisted Clinical Medicine, Heidleberg University, Mannheim, GermanyDepartment of Biomedicine, Mohn Medical Imaging and Visualization Centre, University of Bergen, Bergen, NorwayMedical Faculty Mannheim, Mannheim Institute for Intelligent Systems in Medicine, Computer Assisted Clinical Medicine, Heidleberg University, Mannheim, GermanyMedical Faculty Mannheim, Mannheim Institute for Intelligent Systems in Medicine, Computer Assisted Clinical Medicine, Heidleberg University, Mannheim, GermanyFaculty of Electrical Engineering, University of Tuzla, Tuzla, Bosnia and HerzegovinaDepartment of Biomedicine, Mohn Medical Imaging and Visualization Centre, University of Bergen, Bergen, NorwayInstitute of Electronics, Łódź University of Technology, Lodz, PolandFaculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, SloveniaMagnetic 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.https://ieeexplore.ieee.org/document/9427136/Renal MRIimage segmentationdeep learning |
| spellingShingle | Frank G. Zollner Marek Kocinski Laura Hansen Alena-Kathrin Golla Amira Serifovic Trbalic Arvid Lundervold Andrzej Materka Peter Rogelj Kidney Segmentation in Renal Magnetic Resonance Imaging - Current Status and Prospects IEEE Access Renal MRI image segmentation deep learning |
| title | Kidney Segmentation in Renal Magnetic Resonance Imaging - Current Status and Prospects |
| title_full | Kidney Segmentation in Renal Magnetic Resonance Imaging - Current Status and Prospects |
| title_fullStr | Kidney Segmentation in Renal Magnetic Resonance Imaging - Current Status and Prospects |
| title_full_unstemmed | Kidney Segmentation in Renal Magnetic Resonance Imaging - Current Status and Prospects |
| title_short | Kidney Segmentation in Renal Magnetic Resonance Imaging - Current Status and Prospects |
| title_sort | kidney segmentation in renal magnetic resonance imaging current status and prospects |
| topic | Renal MRI image segmentation deep learning |
| url | https://ieeexplore.ieee.org/document/9427136/ |
| work_keys_str_mv | AT frankgzollner kidneysegmentationinrenalmagneticresonanceimagingcurrentstatusandprospects AT marekkocinski kidneysegmentationinrenalmagneticresonanceimagingcurrentstatusandprospects AT laurahansen kidneysegmentationinrenalmagneticresonanceimagingcurrentstatusandprospects AT alenakathringolla kidneysegmentationinrenalmagneticresonanceimagingcurrentstatusandprospects AT amiraserifovictrbalic kidneysegmentationinrenalmagneticresonanceimagingcurrentstatusandprospects AT arvidlundervold kidneysegmentationinrenalmagneticresonanceimagingcurrentstatusandprospects AT andrzejmaterka kidneysegmentationinrenalmagneticresonanceimagingcurrentstatusandprospects AT peterrogelj kidneysegmentationinrenalmagneticresonanceimagingcurrentstatusandprospects |