TY - JOUR
T1 - Atrous residual interconnected encoder to attention decoder framework for vertebrae segmentation via 3D volumetric CT images
AU - Li, Wenqiang
AU - Tang, Yuk Ming
AU - Wang, Ziyang
AU - Yu, Kai Ming
AU - To, Suet
N1 - Funding Information:
This research is supported by the Research Committee of The Hong Kong Polytechnic University through a studentship (project account code: RK3N ).
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/9
Y1 - 2022/9
N2 - Automatic vertebrae segmentation utilizing Computer Tomography (CT) plays a vital role in automated spine analyses, including the detection of vertebral body fractures and spine deformities assessment. A significant advancement in deep learning (DL) has enabled deep convolutional neural networks (DCNNs) to achieve precise performance in automated vertebrae segmentation. Despite the advantages of semantic segmentation algorithms based on DCNNs, they face limitations such as multi-scale objects, feature loss between the encoder and decoder, lack of medical image data, and limited filter field of view. A novel algorithm is presented that enables automated segmentation of vertebral bodies using volumetric CT images of the spine. The proposed model incorporates an encoder and decoder framework, and utilizes Layer Normalization to enhance the mini-batch training performance. The issue of feature loss between encoder and decoder is addressed by developing an Atrous Residual Path that carries more information from the encoder to the decoder instead of using an easy shortcut. As part of the proposed approach, a 3D Attention Module is designed to extract features from various scales in the decoding stage and further enhance the performance of the decoder. Multiple metrics are used to evaluate the proposed model on a public vertebrae dataset. According to the experimental results, our proposed approach provides competitive performance in comparison with state-of-the-art methods for automatic vertebrae semantic segmentation.
AB - Automatic vertebrae segmentation utilizing Computer Tomography (CT) plays a vital role in automated spine analyses, including the detection of vertebral body fractures and spine deformities assessment. A significant advancement in deep learning (DL) has enabled deep convolutional neural networks (DCNNs) to achieve precise performance in automated vertebrae segmentation. Despite the advantages of semantic segmentation algorithms based on DCNNs, they face limitations such as multi-scale objects, feature loss between the encoder and decoder, lack of medical image data, and limited filter field of view. A novel algorithm is presented that enables automated segmentation of vertebral bodies using volumetric CT images of the spine. The proposed model incorporates an encoder and decoder framework, and utilizes Layer Normalization to enhance the mini-batch training performance. The issue of feature loss between encoder and decoder is addressed by developing an Atrous Residual Path that carries more information from the encoder to the decoder instead of using an easy shortcut. As part of the proposed approach, a 3D Attention Module is designed to extract features from various scales in the decoding stage and further enhance the performance of the decoder. Multiple metrics are used to evaluate the proposed model on a public vertebrae dataset. According to the experimental results, our proposed approach provides competitive performance in comparison with state-of-the-art methods for automatic vertebrae semantic segmentation.
KW - Computed Tomography
KW - Convolutional neural networks
KW - Deep learning
KW - Vertebrae segmentation
UR - http://www.scopus.com/inward/record.url?scp=85133349421&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2022.105102
DO - 10.1016/j.engappai.2022.105102
M3 - Journal article
AN - SCOPUS:85133349421
SN - 0952-1976
VL - 114
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
M1 - 105102
ER -