  Automatic medical image segmentation based on Computed Tomography (CT) has
been widely applied for computer-aided surgery as a prerequisite. With the
development of deep learning technologies, deep convolutional neural networks
(DCNNs) have shown robust performance in automated semantic segmentation of
medical images. However, semantic segmentation algorithms based on DCNNs still
meet the challenges of feature loss between encoder and decoder, multi-scale
object, restricted field of view of filters, and lack of medical image data.
This paper proposes a novel algorithm for automated vertebrae segmentation via
3D volumetric spine CT images. The proposed model is based on the structure of
encoder to decoder, using layer normalization to optimize mini-batch training
performance. To address the concern of the information loss between encoder and
decoder, we designed an Atrous Residual Path to pass more features from encoder
to decoder instead of an easy shortcut connection. The proposed model also
applied the attention module in the decoder part to extract features from
variant scales. The proposed model is evaluated on a publicly available dataset
by a variety of metrics. The experimental results show that our model achieves
competitive performance compared with other state-of-the-art medical semantic
segmentation methods.
