Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort.

Wolfgang Wirth; Felix Eckstein; Jana Kemnitz; Christian Frederik Baumgartner; Ender Konukoglu; David Fuerst; Akshay Sanjay Chaudhari

doi:10.1007/s10334-020-00889-7

Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort.

Wolfgang Wirth^* (Erstautor/-in)
, Felix Eckstein (Co-Autor/-in)
, Jana Kemnitz (Co-Autor/-in)
, Christian Frederik Baumgartner
, Ender Konukoglu
, David Fuerst (Co-Autor/-in)
, Akshay Sanjay Chaudhari

^*Korrespondierende/r Autor/-in für diese Arbeit

Universitätsinstitut für Anatomie und Zellbiologie Salzburg

Publikation: Beitrag in Fachzeitschrift › Originalarbeit › Begutachtung

37 Quellenangaben (Web of Science)

Originalsprache	Englisch
Seiten (von - bis)	337-354
Fachzeitschrift	MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE
Jahrgang	34
Ausgabenummer	3
DOIs	https://doi.org/10.1007/s10334-020-00889-7
Publikationsstatus	Veröffentlicht - 2021

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10.1007/s10334-020-00889-7

1 Abgeschlossen

A Machine- (Deep-) Learning Approach to Fully Automated Regional Bone, Cartilage, and Phenotype Analysis in Knee Osteoarthritis –Development, Validation, Application and Qualification
Wirth, W. (Leitende(r) Forscher/-in) & Kemnitz, J. (CoPI)
1/10/18 → 1/01/20
Projekt: Forschung

Dieses zitieren

Wirth, W., Eckstein, F., Kemnitz, J., Baumgartner, C. F., Konukoglu, E., Fuerst, D., & Chaudhari, A. S. (2021). Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE, 34(3), 337-354. https://doi.org/10.1007/s10334-020-00889-7

Wirth, Wolfgang ; Eckstein, Felix ; Kemnitz, Jana et al. / Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort. in: MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE. 2021 ; Jahrgang 34, Nr. 3. S. 337-354.

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title = "Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort.",

keywords = "KNEE ARTICULAR-CARTILAGE, CLINICAL-TRIALS, THICKNESS, JOINT, PLACEBO, DESIGN, TISSUE",

author = "Wolfgang Wirth and Felix Eckstein and Jana Kemnitz and Baumgartner, \{Christian Frederik\} and Ender Konukoglu and David Fuerst and Chaudhari, \{Akshay Sanjay\}",

note = "Wirth; Eckstein; Kemitz; F{\"u}rst: Paracelsus Med Univ Salzburg *** Nuremberg, Inst Anat *** Cell Biol, Dept Imaging *** Funct Musculoskeletal Res, Strubergasse 21, A-5020 Salzburg, Austria;Paracelsus Med Univ, Ludwig Boltzmann Inst Arthrit *** Rehabil, Salzburg, Austria;",

year = "2021",

doi = "10.1007/s10334-020-00889-7",

language = "English",

volume = "34",

pages = "337--354",

journal = "MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE",

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Wirth, W , Eckstein, F , Kemnitz, J, Baumgartner, CF, Konukoglu, E, Fuerst, D & Chaudhari, AS 2021, 'Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort.', MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE, Jg. 34, Nr. 3, S. 337-354. https://doi.org/10.1007/s10334-020-00889-7

Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort. / Wirth, Wolfgang (Erstautor/-in); Eckstein, Felix (Co-Autor/-in); Kemnitz, Jana (Co-Autor/-in) et al.
in: MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE, Jahrgang 34, Nr. 3, 2021, S. 337-354.

Publikation: Beitrag in Fachzeitschrift › Originalarbeit › Begutachtung

TY - JOUR

T1 - Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort.

AU - Wirth, Wolfgang

AU - Eckstein, Felix

AU - Kemnitz, Jana

AU - Baumgartner, Christian Frederik

AU - Konukoglu, Ender

AU - Fuerst, David

AU - Chaudhari, Akshay Sanjay

N1 - Wirth; Eckstein; Kemitz; Fürst: Paracelsus Med Univ Salzburg *** Nuremberg, Inst Anat *** Cell Biol, Dept Imaging *** Funct Musculoskeletal Res, Strubergasse 21, A-5020 Salzburg, Austria;Paracelsus Med Univ, Ludwig Boltzmann Inst Arthrit *** Rehabil, Salzburg, Austria;

PY - 2021

Y1 - 2021

KW - KNEE ARTICULAR-CARTILAGE

KW - CLINICAL-TRIALS

KW - THICKNESS

KW - JOINT

KW - PLACEBO

KW - DESIGN

KW - TISSUE

U2 - 10.1007/s10334-020-00889-7

DO - 10.1007/s10334-020-00889-7

M3 - Original Article

C2 - 33025284

SN - 0968-5243

VL - 34

SP - 337

EP - 354

JO - MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE

JF - MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE

IS - 3

ER -

Wirth W , Eckstein F , Kemnitz J, Baumgartner CF, Konukoglu E, Fuerst D et al. Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE. 2021;34(3):337-354. doi: 10.1007/s10334-020-00889-7

Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort.

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Projekte

A Machine- (Deep-) Learning Approach to Fully Automated Regional Bone, Cartilage, and Phenotype Analysis in Knee Osteoarthritis –Development, Validation, Application and Qualification

Dieses zitieren