Segmentation of Spine Computed Tomography Images Based on Three-Dimensional Recurrent Residual Convolution

doi:10.19678/j.issn.1000-3428.0067751

Abstract

Abstract:

The automatic segmentation of spine Computed Tomography(CT) images can assist doctors in diagnosing related diseases. Compared to Three-Dimensional(3D) reconstruction after Two-Dimensional(2D) segmentation, the 3D segmentation method is more convenient and can retain the spatial information of the image. To address the problem of the low accuracy of 3D spine segmentation, a U-Net based on 3D recurrent residual convolution to segment CT images of the spine is proposed in this study. A coordination attention mechanism is introduced in the network front to focus the network on the region of interest. A 3D recurrent residual module is used instead of a typical convolution module to accumulate features effectively and mitigate gradient disappearance. An efficient connected hybrid convolution module is added to preserve the tiny features. The dual-feature residual attention module is used instead of the jump connection for multiscale fusion to fuse semantics between high and low levels, and the global context is modeled by aggregating the features of different levels to improve the segmentation performance. First, the model is tested on the public datasets of CSI2014, and compared with other 3D segmentation networks and different spine segmentation methods, the Dice Similarity Coefficient(DSC) reaches 93.85%, which is 1.77-7.65 percentage points higher than those of other six segmentation networks and 1.67-10.85 percentage points higher than those of other spine segmentation methods. The model is also tested on the local lumbar dataset, and the DSC is increased by 1.51-19.86 percentage points compared with those of the other six segmentation models, verifying the effectiveness of the method proposed in this study and the feasibility of applying it to computer-aided diagnosis and treatment.

Key words: spine segmentation, Three-Dimensional(3D) medical image, deep learning, attention mechanism, recurrent residual convolution

摘要：

脊柱计算机断层摄影(CT)图像的自动分割能够辅助医生诊疗相关疾病, 相较于二维分割后再进行三维重建, 三维分割方法更方便且能保留图像的空间信息。针对现有三维脊柱分割方法精度较低的问题, 提出一种以三维循环残差卷积为基础的U型网络对脊柱CT图像进行分割。在网络前端引入三维坐标注意力机制使网络关注感兴趣的区域, 使用三维循环残差模块代替普通卷积模块, 使得网络在有效累积特征的同时缓解梯度消失问题。加入高效密集连接混合卷积模块减少底层细小特征信息的丢失, 提出双特征残差注意力机制代替跳跃连接进行高低层级间的语义融合, 通过聚合不同层级特征对全局上下文进行建模, 提升分割性能。实验结果表明: 在CSI2014公开数据集上, 该网络Dice相似系数(DSC)达到93.85%, 相较于对比的分割网络提升了1.77~7.65个百分点, 相较于其他脊椎分割方法提升了1.67~10.85个百分点; 在本地腰椎数据集上, 相较于对比的分割模型DSC提升了1.51~19.86个百分点, 验证了所提方法的有效性和应用于计算机辅助诊疗的可行性。

关键词: 脊柱分割, 三维医学图像, 深度学习, 注意力机制, 循环残差卷积

Yudan YANG, Junhua ZHANG, Yunfeng LIU. Segmentation of Spine Computed Tomography Images Based on Three-Dimensional Recurrent Residual Convolution[J]. Computer Engineering, 2024, 50(4): 237-246.

杨玉聃, 张俊华, 刘云凤. 基于三维循环残差卷积的脊柱CT图像分割[J]. 计算机工程, 2024, 50(4): 237-246.

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Figures/Tables 15

Fig.1 Automatic segmentation framework of spine CT image

Fig.2 3D coordinate attention mechanism

Fig.3 3D recurrent residual convolution block

Fig.4 Efficient dense-connected hybrid convolution module

Fig.5 Receptive field of feature map with the same convolution kernel and different void ratios

Fig.6 Double-feature residual attention mechanism

Fig.7 Section renderings of predicted images in ablation experiment

Fig.8 3D visualization rendering 1 of CSI2014 dataset comparison experiment

Fig.9 3D visualization rendering 2 of CSI2014 dataset comparison experiment

Fig.10 3D visualization rendering of lumbar dataset comparative experiment

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