Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information

Abstract

Automatic vertebrae identification and localization from arbitrary CT images is challenging. Vertebrae usually share similar morphological appearance. Because of pathology and the arbitrary field-of-view of CT scans, one can hardly rely on the existence of some anchor vertebrae or parametric methods to model the appearance and shape. To solve the problem, we argue that one should make use of the short-range contextual information, such as the presence of some nearby organs (if any), to roughly estimate the target vertebrae; due to the unique anatomic structure of the spine column, vertebrae have fixed sequential order which provides the important long-range contextual information to further calibrate the results. We propose a robust and efficient vertebrae identification and localization system that can inherently learn to incorporate both the short-range and long-range contextual information in a supervised manner. To this end, we develop a multi-task 3D fully convolutional neural network (3D FCN) to effectively extract the short-range contextual information around the target vertebrae. For the long-range contextual information, we propose a multi-task bidirectional recurrent neural network (Bi-RNN) to encode the spatial and contextual information among the vertebrae of the visible spine column. We demonstrate the effectiveness of the proposed approach on a challenging dataset and the experimental results show that our approach outperforms the state-of-the-art methods by a significant margin.

References in corpus (4)

• Fully Convolutional Networks for Semantic Segmentation
• Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
• Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function
• Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images

Cited by in corpus (8)

• VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
• Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization
• Iterative fully convolutional neural networks for automatic vertebra segmentation and identification
• Residual Block-based Multi-Label Classification and Localization Network with Integral Regression for Vertebrae Labeling
• Vertebrae Detection and Localization in CT with Two-Stage CNNs and Dense Annotations
• SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae
• Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-constrained Optimization
• Labelling Vertebrae with 2D Reformations of Multidetector CT Images: An Adversarial Approach for Incorporating Prior Knowledge of Spine Anatomy