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Automatic medical image segmentation based on Computed Tomography (CT) hasbeen widely applied for computer-aided surgery as a prerequisite. With thedevelopment of deep learning technologies, deep convolutional neural networks(DCNNs) have shown robust performance in automated semantic segmentation ofmedical images. However, semantic segmentation algorithms based on DCNNs stillmeet the challenges of feature loss between encoder and decoder, multi-scaleobject, restricted field of view of filters, and lack of medical image data.This paper proposes a novel algorithm for automated vertebrae segmentation via3D volumetric spine CT images. The proposed model is based on the structure ofencoder to decoder, using layer normalization to optimize mini-batch trainingperformance. To address the concern of the information loss between encoder anddecoder, we designed an Atrous Residual Path to pass more features from encoderto decoder instead of an easy shortcut connection. The proposed model alsoapplied the attention module in the decoder part to extract features fromvariant scales. The proposed model is evaluated on a publicly available datasetby a variety of metrics. The experimental results show that our model achievescompetitive performance compared with other state-of-the-art medical semanticsegmentation methods.

Atrous Residual Interconnected Encoder to Attention Decoder Framework for Vertebrae Segmentation via 3D Volumetric CT Images