  Vertebral detection and segmentation are critical steps for treatment
planning in spine surgery and radiation therapy. Accurate identification and
segmentation are complicated in imaging that does not include the full spine,
in cases with variations in anatomy (T13 and/or L6 vertebrae), and in the
presence of fracture or hardware. This paper proposes VertDetect, a fully
automated end-to-end 3D vertebral instance segmentation Convolutional Neural
Network (CNN) model to predict vertebral level labels and segmentations for all
vertebrae present in a CT scan. The utilization of a shared CNN backbone
provides the detection and segmentation branches of the network with feature
maps containing both spinal and vertebral level information. A Graph
Convolutional Network (GCN) layer is used to improve vertebral labelling by
using the known structure of the spine. This model achieved a Dice Similarity
Coefficient (DSC) of 0.883 (95% CI, 0.843-0.906) and 0.882 (95% CI,
0.835-0.909) in the VerSe 2019 and 0.868 (95\% CI, 0.834-0.890) and 0.869 (95\%
CI, 0.832-0.891) in the VerSe 2020 public and hidden test sets, respectively.
This model achieved state-of-the-art performance for an end-to-end
architecture, whose design facilitates the extraction of features that can be
subsequently used for downstream tasks.
