Abstract: Light-weight Semantic Segmentation and Labelling of Vertebrae in 3D-CT Scans

Opportunistic screening of the spine using routine 3D-CT scans could be beneficial for early diagnosis and prevention of osteoporosis and degenerative diseases. In clinical practice, a software that alerts radiologists for signs of osteoporosis and degenerative deformities should be accurate and robust despite being limited by computational resources. We explore a light-weight alternative to existing vertebrae segmentation and labelling algorithms in our two-stage diagnosis pipeline and evaluate the efficiency and robustness of our proposed Deep Learning method. During our first stage, semantic segmentation and labelling of the vertebrae is performed using a low-complexity 3D-CNN. Our efficient architecture includes a MobileNetv2 backbone and DeepLab for segmentation. We optimise the network architecture to improve efficiency by applying the compound scaling idea of the EfficientNet. The first stage of our model is trained and evaluated on the public VerSe dataset, resulting in a multi-label Dice score of 75% while taking less than 0.05s inference time on GPU. The segmentation outcome can be further used to extract centre coordinates of each vertebra which are classified by a 3D-CNN into normal vertebra, degenerative deformities and osteoporotic fractures. Our preliminary results on the DiagnostikBilanz dataset, using centre coordinates, yield an F1-score of 76%. Our fully-automatic pipeline achieves an F1-Score of 74.6%, which is an improvement of 7% compared to the pipeline using nnUNet for segmentation in the first stage. Our method provides a light-weight solution to assist radiologists in differentiating osteoporotic fractures from degenerative deformities in opportunistic screening of the spine in CT scans. In particular, it allows to incorporate segmentation information into the challenging differentiation of degenerative deformities from osteoporotic fractures.