Automated quantification of cartilage quality for hip treatment decision support

Ruckli, Adrian Cyrill; Schmaranzer, Florian; Meier, Malin K; Lerch, Till D; Steppacher, Simon D; Tannast, Moritz; Zeng, Guodong; Burger, Jürgen; Siebenrock, Klaus A; Gerber, Nicolas; Gerber, Kate (2022). Automated quantification of cartilage quality for hip treatment decision support. International journal of computer assisted radiology and surgery, 17(11), pp. 2011-2021. Springer 10.1007/s11548-022-02714-z

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Preservation surgery can halt the progress of joint degradation, preserving the life of the hip; however, outcome depends on the existing cartilage quality. Biochemical analysis of the hip cartilage utilizing MRI sequences such as delayed gadolinium-enhanced MRI of cartilage (dGEMRIC), in addition to morphological analysis, can be used to detect early signs of cartilage degradation. However, a complete, accurate 3D analysis of the cartilage regions and layers is currently not possible due to a lack of diagnostic tools.

A system for the efficient automatic parametrization of the 3D hip cartilage was developed. 2D U-nets were trained on manually annotated dual-flip angle (DFA) dGEMRIC for femoral head localization and cartilage segmentation. A fully automated cartilage sectioning pipeline for analysis of central and peripheral regions, femoral-acetabular layers, and a variable number of section slices, was developed along with functionality for the automatic calculation of dGEMRIC index, thickness, surface area, and volume.

The trained networks locate the femoral head and segment the cartilage with a Dice similarity coefficient of 88 ± 3 and 83 ± 4% on DFA and magnetization-prepared 2 rapid gradient-echo (MP2RAGE) dGEMRIC, respectively. A completely automatic cartilage analysis was performed in 18s, and no significant difference for average dGEMRIC index, volume, surface area, and thickness calculated on manual and automatic segmentation was observed.

An application for the 3D analysis of hip cartilage was developed for the automated detection of subtle morphological and biochemical signs of cartilage degradation in prognostic studies and clinical diagnosis. The segmentation network achieved a 4-time increase in processing speed without loss of segmentation accuracy on both normal and deformed anatomy, enabling accurate parametrization. Retraining of the networks with the promising MP2RAGE protocol would enable analysis without the need for B1 inhomogeneity correction in the future.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > Faculty Institutions > sitem Center for Translational Medicine and Biomedical Entrepreneurship
04 Faculty of Medicine > Department of Orthopaedic, Plastic and Hand Surgery (DOPH) > Clinic of Orthopaedic Surgery
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology
08 Faculty of Science > School of Biomedical and Precision Engineering (SBPE)
08 Faculty of Science > School of Biomedical and Precision Engineering (SBPE) > Personalised Medicine

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Ruckli, Adrian Cyrill, Schmaranzer, Florian, Meier, Malin Kristin, Lerch, Till, Steppacher, Simon Damian, Zeng, Guodong, Burger, Jürgen, Siebenrock, Klaus-Arno, Gerber, Nicolas, Gerber, Kate


600 Technology > 610 Medicine & health
600 Technology > 620 Engineering






[4] Swiss National Science Foundation




Nicolas Gerber

Date Deposited:

18 Aug 2022 12:47

Last Modified:

24 Oct 2023 10:56

Publisher DOI:


PubMed ID:


Additional Information:

Swiss National Science Foundation Grant Number 205091




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