VLD-Net: Localization and Detection of the Vertebrae From X-Ray Images by Reinforcement Learning With Adaptive Exploration Mechanism and Spine Anatomy Information

Abstract

Accurate and efficient vertebrae localization and detection in X-ray images are essential for diagnosing and treating spinal diseases. However, most existing methods struggle with the complexity of spine X-ray images, yielding inaccurate results due to insufficient utilization of spinal anatomy information and neglect of individual vertebra characteristics. In this paper, we propose an innovative Vertebrae Localization and Detection Network (VLD-Net) to accurately assist physicians in diagnosing spine-related diseases from X-ray images. Our VLD-Net, for the first time, defines vertebrae localization as a top-bottom sequential decision-making process, employing deep reinforcement learning (DRL) to fully leverage the anatomical information of the spine. Simultaneously, it also prioritizes the distinct characteristics of each vertebra for accurate detection. Specifically, VLD-Net combines three key components: 1) An advanced vertebrae localization module based on DRL is proposed, effectively leveraging anatomical information of the spine. 2) A novel adaptive exploration mechanism is coined to understand the behavior of the DRL agent during training, pinpointing how to effectively achieve the trade-off between exploration and exploitation. 3) An innovative vertebra-focused module is proposed to accurately detect vertebral landmarks, using the attention region of each vertebra as input to enhance focus on the target and reduce interference from surrounding tissue. Extensive experiments on two public spine datasets demonstrate that the VLD-Net outperforms the state-of-the-art methods in accuracy and robustness.