# Iterative fully convolutional neural networks for automatic vertebra segmentation and identification

arXiv: https://arxiv.org/abs/1804.04383 · HTML: https://mlsanity.com/paper/1804.04383/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2018.

## Built on by (most cited first, 25 of 25)

- [CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography](https://mlsanity.com/paper/2105.14711/) (2021)
- [CLIP in Medical Imaging: A Survey](https://mlsanity.com/paper/2312.07353/) (2023)
- [Lumbar spine segmentation in MR images: a dataset and a public benchmark](https://mlsanity.com/paper/2306.12217/) (2023)
- [A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data](https://mlsanity.com/paper/2103.06360/) (2021)
- [Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle](https://mlsanity.com/paper/2110.12177/) (2021)
- [Multi-View Vertebra Localization and Identification from CT Images](https://mlsanity.com/paper/2307.12845/) (2023)
- [SLoRD: Structural Low-Rank Descriptors for Shape Consistency in Vertebrae Segmentation](https://mlsanity.com/paper/2407.08555/) (2024)
- [Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding](https://mlsanity.com/paper/2212.02014/) (2022)
- [Robust vertebra identification using simultaneous node and edge predicting Graph Neural Networks](https://mlsanity.com/paper/2308.02509/) (2023)
- [Semi-Supervised Hybrid Spine Network for Segmentation of Spine MR Images](https://mlsanity.com/paper/2203.12151/) (2022)
- [Employing similarity to highlight differences: On the impact of anatomical assumptions in chest X-ray registration methods](https://mlsanity.com/paper/2301.09338/) (2023)
- [Technical Report: Quality Assessment Tool for Machine Learning with Clinical CT](https://mlsanity.com/paper/2107.12842/) (2021)
- [Spatial Correspondence between Graph Neural Network-Segmented Images](https://mlsanity.com/paper/2303.06550/) (2023)
- [VertDetect: Fully End-to-End 3D Vertebral Instance Segmentation Model](https://mlsanity.com/paper/2311.09958/) (2023)
- [Frequency-enhanced Multi-granularity Context Network for Efficient Vertebrae Segmentation](https://mlsanity.com/paper/2506.23086/) (2025)
- [SIMSPINE: A Biomechanics-Aware Simulation Framework for 3D Spine Motion Annotation and Benchmarking](https://mlsanity.com/paper/2602.20792/) (2026)
- [DoubleU-Net++: Architecture with Exploit Multiscale Features for Vertebrae Segmentation](https://mlsanity.com/paper/2201.12389/) (2022)
- [Intelligent Scoliosis Screening and Diagnosis: A Survey](https://mlsanity.com/paper/2310.08756/) (2023)
- [Intelligent Cervical Spine Fracture Detection Using Deep Learning Methods](https://mlsanity.com/paper/2311.05708/) (2023)
- [Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-propagation](https://mlsanity.com/paper/2402.08892/) (2024)
- [Advances in Medical Image Segmentation: A Comprehensive Survey with a Focus on Lumbar Spine Applications](https://mlsanity.com/paper/2510.03318/) (2025)
- [EXR: An Interactive Immersive EHR Visualization in Extended Reality](https://mlsanity.com/paper/2512.05438/) (2025)
- [Tracing 3D Anatomy in 2D Strokes: A Multi-Stage Projection Driven Approach to Cervical Spine Fracture Identification](https://mlsanity.com/paper/2601.15235/) (2026)
- [Enhancing Low Back Pain Assessment with Diffusion Models for Lumbar Spine MRI Segmentation](https://mlsanity.com/paper/2608.04906/) (2026)
- [Extending TotalSegmentator: Predicting Patient and Acquisition Characteristics from CT and MR Images](https://mlsanity.com/paper/2608.29348/) (2026)

## Abstract (arXiv)

Precise segmentation and anatomical identification of the vertebrae provides the basis for automatic analysis of the spine, such as detection of vertebral compression fractures or other abnormalities. Most dedicated spine CT and MR scans as well as scans of the chest, abdomen or neck cover only part of the spine. Segmentation and identification should therefore not rely on the visibility of certain vertebrae or a certain number of vertebrae. We propose an iterative instance segmentation approach that uses a fully convolutional neural network to segment and label vertebrae one after the other, independently of the number of visible vertebrae. This instance-by-instance segmentation is enabled by combining the network with a memory component that retains information about already segmented vertebrae. The network iteratively analyzes image patches, using information from both image and memory to search for the next vertebra. To efficiently traverse the image, we include the prior knowledge that the vertebrae are always located next to each other, which is used to follow the vertebral column. This method was evaluated with five diverse datasets, including multiple modalities (CT and MR), various fields of view and coverages of different sections of the spine, and a particularly challenging set of low-dose chest CT scans. The proposed iterative segmentation method compares favorably with state-of-the-art methods and is fast, flexible and generalizable.