Participants
The study was approved by the local institutional committee for human research and in accordance with the 1964 Helsinki declaration and its later amendments. All individuals gave written informed consent before participation in the study.
Ten healthy individuals were recruited for this study (eight men and two women, age 29 ± 8 years [mean ± standard deviation (SD)], and body mass index 26.7 ± 2.3 kg/m2 [mean ± SD]).
MRI protocol
All participants underwent MRI at two time points (baseline and six-week follow-up) to obtain longitudinal imaging data for long-term reproducibility purposes. The lumbar musculature of the individual was scanned on a 3-T whole-body scanner (Ingenia, Philips Healthcare, Best, The Netherlands) using the built-in-the-table posterior coil elements (12-channel array). An axially prescribed six-echo three-dimensional spoiled gradient-echo sequence was used for chemical shift encoding-based water-fat separation. The sequence acquired the six echoes in a single time of repetition using non-flyback (bipolar) read-out gradients and the following imaging parameters: time of repetition 11 ms; minimum time of echo 1.04 ms; ΔTE 0.8 ms; field of view 220 × 220 × 219 mm; acquisition matrix 72 × 110 × 73; acquisition voxel size 3.1 × 2.0 × 3.0 mm; frequency encoding direction left to right; receiver bandwidth 2756 Hz/pixel; scan time 2:01 min. A flip angle of 3° was used to minimise T1-bias effects [11].
Image-based fat quantification
The gradient-echo imaging data were processed on-line using the mDIXON Quant software provided by the manufacturer. It performs a complex-based water-fat decomposition using a pre-calibrated seven-peak fat spectrum and a single T2* to model the signal variation with echo time. PDFF maps were then computed as the ratio of the fat signal over the sum of fat and water signals.
Manual segmentation
Manual segmentation of the paraspinal muscles was performed on the PDFF maps at baseline and follow-up by using the free open-source software Medical Imaging Interaction Toolkit (MITK), developed by the Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany (www.mitk.org). The following six muscle compartments were separately segmented by one operator from the upper endplate level of L2 to the lower endplate level of L5: right and left psoas muscles; right and left quadratus lumborum muscles; and right and left erector spinae muscles (Fig. 1a).
Automatic segmentation
Baseline and follow-up images and corresponding manual muscle segmentations of seven individuals were used as a training dataset and those of the remaining three individuals as a test dataset. Manual muscle segmentations in the baseline and follow-up images of the three test participants served as ground truth and were considered as gold standard for the automatic muscle segmentation results. Based on the manual segmentations of the training set, a model of the six muscle compartments was generated. It comprised an average-shaped model, represented as triangle mesh, a dual-feature model, associating each surface triangle with a fat and water image appearance feature, and a detection model [12]. For its generation, first a shape model was created. A fuzzy averaging approach as described in Blaffert et al. [13] was followed to convert the label images resulting from the manual segmentation step to an average multi-compartment surface model. Second, a feature model that relates surface positions with corresponding image features, such as intensity edges, was generated following Peters et al. [14]. To do so, a training set of images with corresponding mesh models was created by adapting the average-shaped model from the previous step to the manual segmentation results. In this step, a simplified version of the model-based segmentation method [15] was applied, using a simple gradient feature to adapt the average-shaped model to the manually created label images. During the training phase, optimal local features are determined for the fat as well as the water image.
For the automatic segmentation of an unseen image, first a generalised Hough transform for structure localisation is performed to initialise the model in the patient image, followed by a coarse-to-fine individualisation of the surface model [15, 16]. During individualisation, an objective function consisting of an image feature match term and a shape deviation term was evaluated for optimising pose and shape of the model. Two image features, for water and fat image, were evaluated simultaneously. The procedure was iterated, allowing first only a rigid transformation of the model and later a free-form deformation to obtain a detailed muscle delineation (Fig. 1b and c; Additional file 1).
Statistical analysis
Dice coefficients [17] were determined to compare the automatic muscle segmentations with the corresponding ground truth. Wilcoxon signed rank tests were used to assess differences of muscle volume and PDFF based on automatic segmentation and ground truth, respectively.