Deep learning is currently widely applied in medical image processing and has achieved good results. However, recognizing vertebrae via image processing remains a challenging problem due to their complex spatial structures. CapsNet is a newly proposed network whose characteristics compensate for some shortcomings of traditional CNNs, and it has been shown to perform well on many tasks, including medical image recognition. In this paper, we applied a modified CapsNet to recognise 3D vertebral images by introducing an RNN module into CapsNet to further enhance its learning ability. This new network is called RNNinCaps, and it achieves the highest recognition performance on 3D vertebral images (the average accuracy of RNNinCaps exceeds the accuracy of the original CapsNet by 46.2% and that of a traditional CNN by 12.6%). RNNinCaps also performs better than several mainstream networks. RNNinCaps can promotes CapsNet’s application in the field of 3D medical image recognition.

深度学习目前已广泛应用于医学图像处理，并取得了良好的效果。然而，由于其复杂的空间结构，通过图像处理识别椎骨仍然是一个具有挑战性的问题。CapsNet是一个新提出的网络，其特征弥补了传统CNN的一些缺点，并且已经证明在许多任务（包括医学图像识别）上都能表现出色。在本文中，我们通过在CapsNet中引入RNN模块以进一步增强其学习能力，将经过修改的CapsNet应用于识别3D椎骨图像。这个新网络称为RNNinCaps，它在3D椎骨图像上实现了最高的识别性能（RNNinCaps的平均精度比原始CapsNet的精度高46.2％，比传统CNN的精度高12.6％）。RNNinCaps的性能也优于几个主流网络。RNNinCaps可以促进CapsNet在3D医学图像识别领域的应用。