2021
DOI: 10.1148/ryai.2021200213
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Automated Morphometric Analysis of the Hip Joint on MRI from the German National Cohort Study

Abstract: To develop and validate an automated morphometric analysis framework for the quantitative analysis of geometric hip joint parameters in MR images from the German National Cohort (GNC) study. Materials and Methods:A secondary analysis on 40 participants (mean age, 51 years; age range, 30-67 years; 25 women) from the prospective GNC MRI study (2015-2016) was performed. Based on a proton density-weighted three-dimensional fast spin-echo sequence, a morphometric analysis approach was developed, including deep lear… Show more

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Cited by 4 publications
(2 citation statements)
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“…But most of these studies were performed based on plain radiographic images [28][29][30], ultrasound images [31], or computed tomography (CT) images [32]. Fischer et al investigated an automated morphometric analysis framework for the quantitative analysis of geometric hip joint parameters in MR images from the German National Cohort (GNC) study [33]. Their secondary analysis on 40 participants (mean age, 51 years; age range, 30-67 years; 25 women) involved a morphometric assessment, which was based on a proton density-weighted 3D fast spin-echo sequence, calculating the centrumcollum-diaphyseal, center-edge (CE), three alpha angles, head-neck offset (HNO), and HNO ratio along with the acetabular depth, inclination, and anteversion.…”
Section: Discussionmentioning
confidence: 99%
“…But most of these studies were performed based on plain radiographic images [28][29][30], ultrasound images [31], or computed tomography (CT) images [32]. Fischer et al investigated an automated morphometric analysis framework for the quantitative analysis of geometric hip joint parameters in MR images from the German National Cohort (GNC) study [33]. Their secondary analysis on 40 participants (mean age, 51 years; age range, 30-67 years; 25 women) involved a morphometric assessment, which was based on a proton density-weighted 3D fast spin-echo sequence, calculating the centrumcollum-diaphyseal, center-edge (CE), three alpha angles, head-neck offset (HNO), and HNO ratio along with the acetabular depth, inclination, and anteversion.…”
Section: Discussionmentioning
confidence: 99%
“…As research on deep learning (DL) continues to increase, it has been widely used in tasks such as disease identification, classification, and diagnosis, and some studies have proven that the performance of DL is comparable to that of experienced radiologists [ 13 15 ]. Recent studies have used DL for quantitative measurement tasks in radiology [ 16 , 17 ]. The research results showed that the use of DL for automatic quantitative measurement can improve the consistency and objectivity of the measurement results and save time.…”
Section: Introductionmentioning
confidence: 99%