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

arXiv: https://arxiv.org/abs/1812.03500 · HTML: https://mlsanity.com/paper/1812.03500/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2018.

## Built on by (most cited first, 7 of 7)

- [Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle](https://mlsanity.com/paper/2110.12177/) (2021)
- [SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae](https://mlsanity.com/paper/2110.15082/) (2021)
- [Robust vertebra identification using simultaneous node and edge predicting Graph Neural Networks](https://mlsanity.com/paper/2308.02509/) (2023)
- [VERIDAH: Solving Enumeration Anomaly Aware Vertebra Labeling across Imaging Sequences](https://mlsanity.com/paper/2601.14066/) (2026)
- [VertMatch: A Semi-supervised Framework for Vertebral Structure Detection in 3D Ultrasound Volume](https://mlsanity.com/paper/2212.14747/) (2022)
- [Topology-inspired Cross-domain Network for Developmental Cervical Stenosis Quantification](https://mlsanity.com/paper/2309.06825/) (2023)
- [SpineContextResUNet: A Computationally Efficient Residual UNet for Spine CT Segmentation](https://mlsanity.com/paper/2605.20760/) (2026)

## Abstract (arXiv)

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.