College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, 211106, China.
Department of Orthopedics, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.
Comput Med Imaging Graph. 2022 Sep;100:102109. doi: 10.1016/j.compmedimag.2022.102109. Epub 2022 Aug 6.
Accurate segmentation of the lumbosacral plexus is a crucial step for diagnosis and analysis of nerve damage in clinical. Due to the extremely low contrast and complicated structure around the lumbosacral plexus, it has been remaining a challenging task to effectively segment the lumbosacral plexus from spinal MR images. Even though several deep learning methods for spine segmentation have been developed, most of them only pay attention to the segmentation of vertebral bodies and intervertebral discs rather than nerves. To solve these problems, in this paper, we propose a residual-atrous attention network (RA-Net) for lumbosacral plexus segmentation with MR images. Specifically, the RA-Net consists of three main parts, (1) the atrous encoder module is employed to learn multi-scale contextual features from MR images in the encoder, (2) the residual skip connection operation is used to integrate the features with high-resolution spatial details in the encoder and the high-level contextual features in the decoder, and (3) the scale attention block is proposed for fusing the multi-scale high-level features in the decoder. We perform our proposed RA-Net for the lumbosacral plexus segmentation on the collected spinal MRI dataset with 10 patients (a total of 236 MRI scans). Extensive experiments demonstrate that our RA-Net achieves better performance in lumbosacral plexus segmentation with MR images when compared with several state-of-the-art methods.
准确分割腰骶丛是临床诊断和分析神经损伤的关键步骤。由于腰骶丛周围对比度极低且结构复杂，因此有效地从脊髓磁共振图像中分割腰骶丛仍然是一项具有挑战性的任务。尽管已经开发出几种用于脊柱分割的深度学习方法，但它们大多只关注椎体和椎间盘的分割，而不是神经。为了解决这些问题，本文提出了一种基于 MR 图像的用于腰骶丛分割的残差空洞注意网络（RA-Net）。具体来说，RA-Net 由三个主要部分组成：（1）空洞编码器模块用于从编码器中的 MR 图像中学习多尺度上下文特征；（2）残差跳过连接操作用于在编码器中整合具有高分辨率空间细节的特征和解码器中的高级上下文特征；（3）尺度注意力块用于融合解码器中的多尺度高级特征。我们在收集的包含 10 名患者（共 236 次 MRI 扫描）的脊髓 MRI 数据集上对我们提出的 RA-Net 进行了腰骶丛分割实验。大量实验表明，与几种最先进的方法相比，我们的 RA-Net 在基于 MR 图像的腰骶丛分割中具有更好的性能。
