# Atrous Residual Interconnected Encoder to Attention Decoder Framework for Vertebrae Segmentation via 3D Volumetric CT Images Source: [Atrous Residual Interconnected Encoder to Attention Decoder Framework for Vertebrae Segmentation via 3D Volumetric CT Images]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:05:39.232Z Authors: Wenqiang Li, YM Tang, Ziyang Wang, KM Yu, Sandy To Published: 2021-04-08T12:09:16.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()