Abstract
Intervertebral disc disease, a prevalent ailment, frequently leads to
intermittent or persistent low back pain, and diagnosing and assessing of this
disease rely on accurate measurement of vertebral bone and intervertebral disc
geometries from lumbar MR images. Deep neural network (DNN) models may assist
clinicians with more efficient image segmentation of individual instances
(disks and vertebrae) of the lumbar spine in an automated way, which is termed
as instance image segmentation. In this work, we proposed SymTC, an innovative
lumbar spine MR image segmentation model that combines the strengths of
Transformer and Convolutional Neural Network (CNN). Specifically, we designed a
parallel dual-path architecture to merge CNN layers and Transformer layers, and
we integrated a novel position embedding into the self-attention module of
Transformer, enhancing the utilization of positional information for more
accurate segmentation. To further improves model performance, we introduced a
new data augmentation technique to create synthetic yet realistic MR image
dataset, named SSMSpine, which is made publicly available. We evaluated our
SymTC and the other 15 existing image segmentation models on our private
in-house dataset and the public SSMSpine dataset, using two metrics, Dice
Similarity Coefficient and 95% Hausdorff Distance. The results show that our
SymTC has the best performance for segmenting vertebral bones and
intervertebral discs in lumbar spine MR images. The SymTC code and SSMSpine
dataset are available at https://github.com/jiasongchen/SymTC.