Intra-operator Repeatability of Manual Segmentations of the Hip Muscles on Clinical Magnetic Resonance Images.
Giorgio DavicoFrancesca BottinAlberto Di MartinoVanita CastafaroFabio BaruffaldiCesare FaldiniMarco VicecontiPublished in: Journal of digital imaging (2022)
The manual segmentation of muscles on magnetic resonance images is the gold standard procedure to reconstruct muscle volumes from medical imaging data and extract critical information for clinical and research purposes. (Semi)automatic methods have been proposed to expedite the otherwise lengthy process. These, however, rely on manual segmentations. Nonetheless, the repeatability of manual muscle volume segmentations performed on clinical MRI data has not been thoroughly assessed. When conducted, volumetric assessments often disregard the hip muscles. Therefore, one trained operator performed repeated manual segmentations (n = 3) of the iliopsoas (n = 34) and gluteus medius (n = 40) muscles on coronal T1-weighted MRI scans, acquired on 1.5 T scanners on a clinical population of patients elected for hip replacement surgery. Reconstructed muscle volumes were divided in sub-volumes and compared in terms of volume variance (normalized variance of volumes - nVV), shape (Jaccard Index-JI) and surface similarity (maximal Hausdorff distance-HD), to quantify intra-operator repeatability. One-way repeated measures ANOVA (or equivalent) tests with Bonferroni corrections for multiple comparisons were conducted to assess statistical significance. For both muscles, repeated manual segmentations were highly similar to one another (nVV: 2-6%, JI > 0.78, HD < 15 mm). However, shape and surface similarity were significantly lower when muscle extremities were included in the segmentations (e.g., iliopsoas: HD -12.06 to 14.42 mm, P < 0.05). Our findings show that the manual segmentation of hip muscle volumes on clinical MRI scans provides repeatable results over time. Nonetheless, extreme care should be taken in the segmentation of muscle extremities.
Keyphrases
- magnetic resonance
- contrast enhanced
- deep learning
- skeletal muscle
- convolutional neural network
- healthcare
- magnetic resonance imaging
- computed tomography
- machine learning
- total hip arthroplasty
- high resolution
- coronary artery disease
- diffusion weighted imaging
- end stage renal disease
- prognostic factors
- oxidative stress
- palliative care
- blood pressure
- heart rate
- electronic health record
- chronic kidney disease
- social media
- resistance training
- coronary artery bypass
- chronic pain
- optical coherence tomography
- anti inflammatory
- data analysis
- health insurance
- patient reported outcomes
- network analysis