# Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling

arXiv: https://arxiv.org/abs/2108.06554 · HTML: https://mlsanity.com/paper/2108.06554/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 14 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI](https://mlsanity.com/paper/2007.02606/) (2020)
- [Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization](https://mlsanity.com/paper/1705.05998/) (2017)
- [ivadomed: A Medical Imaging Deep Learning Toolbox](https://mlsanity.com/paper/2010.09984/) (2020)
- [DeepSPINE: Automated Lumbar Vertebral Segmentation, Disc-level Designation, and Spinal Stenosis Grading Using Deep Learning](https://mlsanity.com/paper/1807.10215/) (2018)
- [Spine intervertebral disc labeling using a fully convolutional redundant counting model](https://mlsanity.com/paper/2003.04387/) (2020)

## Built on by (most cited first, 8 of 8)

- [TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation](https://mlsanity.com/paper/2208.00713/) (2022)
- [TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model](https://mlsanity.com/paper/2207.13415/) (2022)
- [Medical Image Segmentation on MRI Images with Missing Modalities: A Review](https://mlsanity.com/paper/2203.06217/) (2022)
- [Dilated-UNet: A Fast and Accurate Medical Image Segmentation Approach using a Dilated Transformer and U-Net Architecture](https://mlsanity.com/paper/2304.11450/) (2023)
- [Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach](https://mlsanity.com/paper/2204.02943/) (2022)
- [HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling](https://mlsanity.com/paper/2311.12486/) (2023)
- [Automatic Segmentation of the Spinal Cord Nerve Rootlets](https://mlsanity.com/paper/2402.00724/) (2024)
- [DFEN: Dual Feature Equalization Network for Medical Image Segmentation](https://mlsanity.com/paper/2505.05913/) (2025)

## Abstract (arXiv)

Labeling vertebral discs from MRI scans is important for the proper diagnosis of spinal related diseases, including multiple sclerosis, amyotrophic lateral sclerosis, degenerative cervical myelopathy and cancer. Automatic labeling of the vertebral discs in MRI data is a difficult task because of the similarity between discs and bone area, the variability in the geometry of the spine and surrounding tissues across individuals, and the variability across scans (manufacturers, pulse sequence, image contrast, resolution and artefacts). In previous studies, vertebral disc labeling is often done after a disc detection step and mostly fails when the localization algorithm misses discs or has false positive detection. In this work, we aim to mitigate this problem by reformulating the semantic vertebral disc labeling using the pose estimation technique. To do so, we propose a stacked hourglass network with multi-level attention mechanism to jointly learn intervertebral disc position and their skeleton structure. The proposed deep learning model takes into account the strength of semantic segmentation and pose estimation technique to handle the missing area and false positive detection. To further improve the performance of the proposed method, we propose a skeleton-based search space to reduce false positive detection. The proposed method evaluated on spine generic public multi-center dataset and demonstrated better performance comparing to previous work, on both T1w and T2w contrasts. The method is implemented in ivadomed (https://ivadomed.org).