Lumbar muscle and vertebral bodies segmentation of chemical shift encoding-based water-fat MRI: the reference database MyoSegmenTUM spine

The database contains 54 MRI datasets of healthy, Caucasian volunteers (15 males, 39 female, age: 51.6 ± 16.7 years).

Fifty-four healthy volunteers underwent MRI on a 3 T system (Ingenia, Philips Healthcare, Best, Netherlands) using a whole-body coil, the built-in 12-channel posterior coil and a 16-channel anterior coil. Subjects were positioned head-first in a supine position. Two different sequences were used for imaging the lumbar muscles and vertebral bodies. The conducted scanning protocol included dedicated scanning parameters, which are shown in Table 1.

The acquired data were analyzed using the mDIXON fat quantification method provided by the manufacturer. A complex-based water-fat decomposition was performed using a single T₂* correction and a pre-calibrated fat spectrum accounting for the presence of the multiple peaks in the fat spectrum [24]. A seven-peak fat spectrum model was employed. The imaging-based proton density fat fraction (PDFF) map was computed as the ratio of the fat signal over the sum of fat and water signals, described previously by Schlaeger et al. [25, 26].

Axial and sagittal water, fat and PDFF images were stored as separate datasets for each subject as a *.dcm file.

The subsequent segmentation of the computed PDFF was performed manually using the open access image viewer software MITK (Medical Imaging Interaction Toolkit, Heidelberg, Germany). All segmentations were performed by a board certified radiologist.

The vertebral bodies L1 to L5 were segmented in the sagittal PDFF maps excluding the posterior elements (Fig. 1).

The axial PDFF maps were used to separately segment the erector spinae and the psoas muscle on both sides from the cranial part of L2 to caudal part of L5. Figure 2 exemplarily shows the depicted ROI in the corresponding muscles.

PDFF of each vertebral body (L1 to L5) and muscle group (right/left erector spinae and psoas muscle, respectively) were extracted. The segmentations of each vertebral body and muscle group is available as a binary mask. Each mask was stored as a separate *.mha file.

The database is available online at https://​osf.​io/​3j54b/​?​view_​only=​f5089274d4a449cd​a2fef1d2df0ecc56​. Axial and sagittal water, fat and PDFF images are deposited as separate datasets for each subject as a *.dcm file. The segmentation masks of each vertebra (L1 to L5) and muscle group were deposited as *.mha files.

Datasets of subjects and corresponding segmentation masks are labeled with the same subject ID (1 to 54). Masks were labeled as L1 to L5 or muscle group (right erector spinae muscle, left erector spinae muscle, right psoas muscle, and left psoas muscle, respectively).

Subject characteristics (sex, age [years], weight [kg], height [m] and body mass index (BMI) [kg/m²]) and PDFF values [%] of the vertebral bone marrow (L1 to L5) and the lumbar muscle groups (right/left erector spinae and psoas muscle, respectively) are listed in Table 2. Segmentation of the paraspinal muscles and the lumbar vertebrae (L1 to L5) amounted 50 min and 1:40 h for each subject, respectively.

Recent and future trends of imaging automatization are and surely will be influencing many aspects of radiological diagnostics now and in the future. Subspecialties as neuroradiology, breast imaging, musculoskeletal radiology and oncological imaging are affected regarding (semi-) automated initial diagnostic finding, disease monitoring and report classification [18, 27‐29]. Our aim with offering a database for manually segmented lumbar muscles and vertebral bodies (L1 to L5) in MR images of 54 healthy volunteers is to provide ground truth data which supports testing and refining newly developed computer vision or machine learning algorithms for automatic lumbar muscle and spine segmentation. The present database offers access to water, fat and PDFF maps.

With the extractable volume and PDFF values for the psoas and erector spinae muscles and the vertebral bodies L1 to L5, we elucidate the use of quantitative MRI in spine and muscle imaging and offer a ground truth dataset for automatic algorithm testing purposes. Figure 1 exemplarily illustrates the PDFF map of the vertebral bodies L1 to L5 and Fig. 2 shows a PDFF map for the respective muscle groups. The presented data is in line with the studies of Schlaeger et al. and Baum et al. and could help to develop more efficient ways of segmenting musculoskeletal structures [25, 30]. This is necessary to enable scientists and clinicians to perform data processing and analysis in an economic way. Additionally, the described methodical aspects are of great importance in order to being able to transfer automatized quantitative MRI into clinical routine by promoting computational segmenting and analyzing PDFF maps. In a further step the engineering of a fully automatic diagnosis finding due to pattern based deep learning could be possible and highly beneficial to diagnostic accuracy, interreader reliability and longitudinal comparability (Fig. 3) [31]. As recently shown in skin cancer diagnostics huge amounts of datasets are needed to constitute an algorithm quality that can outperform a specialist’s evaluation [28]. The amount of data generated with each qualitative respectively quantitative MRI scan delineates information which needs to be assessed carefully and precisely. This sets the high standards to every algorithm competing with specialists’ knowledge and skills. Complex disease entities like neuromuscular disorders, osteoporosis or lower back pain can be represented in different anatomical structures from very subtle bony or muscular alterations to striking pathologic imaging findings in cases of fractures or muscle atrophy [2, 5, 32, 33].

This database should be considered as one contribution to an ongoing process of rationalization and automatization. The limitation of the present database is the inclusion of healthy subjects only. Thus, this database has to be extended by diseased subjects in the future.