SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images
  using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation
Hendrik Möller Robert Graf Joachim Schmitt Benjamin Keinert Matan Atad Anjany Sekuboyina Felix Streckenbach Hanna Schön Florian Kofler Thomas Kroencke Stefanie Bette Stefan Willich Thomas Keil Thoralf Niendorf Tobias Pischon Beate Endemann Bjoern Menze Daniel Rueckert Jan S. Kirschke
Purpose. To present SPINEPS, an open-source deep learning approach for
semantic and instance segmentation of 14 spinal structures (ten vertebra
substructures, intervertebral discs, spinal cord, spinal canal, and sacrum) in
whole body T2w MRI.
  Methods. During this HIPPA-compliant, retrospective study, we utilized the
public SPIDER dataset (218 subjects, 63% female) and a subset of the German
National Cohort (1423 subjects, mean age 53, 49% female) for training and
evaluation. We combined CT and T2w segmentations to train models that segment
14 spinal structures in T2w sagittal scans both semantically and instance-wise.
Performance evaluation metrics included Dice similarity coefficient, average
symmetrical surface distance, panoptic quality, segmentation quality, and
recognition quality. Statistical significance was assessed using the Wilcoxon
signed-rank test. An in-house dataset was used to qualitatively evaluate
out-of-distribution samples.
  Results. On the public dataset, our approach outperformed the baseline
(instance-wise vertebra dice score 0.929 vs. 0.907, p-value<0.001). Training on
auto-generated annotations and evaluating on manually corrected test data from
the GNC yielded global dice scores of 0.900 for vertebrae, 0.960 for
intervertebral discs, and 0.947 for the spinal canal. Incorporating the SPIDER
dataset during training increased these scores to 0.920, 0.967, 0.958,
respectively.
  Conclusions. The proposed segmentation approach offers robust segmentation of
14 spinal structures in T2w sagittal images, including the spinal cord, spinal
canal, intervertebral discs, endplate, sacrum, and vertebrae. The approach
yields both a semantic and instance mask as output, thus being easy to utilize.
This marks the first publicly available algorithm for whole spine segmentation
in sagittal T2w MR imaging.