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

• URL: http://arxiv.org/abs/2104.03715v1
• Date: Thu, 8 Apr 2021 12:09:16 GMT
• Title: Atrous Residual Interconnected Encoder to Attention Decoder Framework for Vertebrae Segmentation via 3D Volumetric CT Images
• Authors: Wenqiang Li, YM Tang, Ziyang Wang, KM Yu, Sandy To
• Abstract summary: 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. The experimental results show that our model achieves competitive performance compared with other state-of-the-art medical semantic segmentation methods.
• Score: 1.8146155083014204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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