# Topology-inspired Cross-domain Network for Developmental Cervical Stenosis Quantification

arXiv: https://arxiv.org/abs/2309.06825 · HTML: https://mlsanity.com/paper/2309.06825/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 45 references that are in the map.
- First posted on arXiv 13 Sep 2023.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Cascaded Pyramid Network for Multi-Person Pose Estimation](https://mlsanity.com/paper/1711.07319/) (2017)
- [Look at Boundary: A Boundary-Aware Face Alignment Algorithm](https://mlsanity.com/paper/1805.10483/) (2018)
- [Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression](https://mlsanity.com/paper/1904.07399/) (2019)
- [Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation](https://mlsanity.com/paper/2111.01236/) (2021)
- [Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization](https://mlsanity.com/paper/1908.00748/) (2019)
- [Numerical Coordinate Regression with Convolutional Neural Networks](https://mlsanity.com/paper/1801.07372/) (2018)
- [Multi-domain Integrative Swin Transformer network for Sparse-View Tomographic Reconstruction](https://mlsanity.com/paper/2111.14831/) (2021)
- [Vertebra-Focused Landmark Detection for Scoliosis Assessment](https://mlsanity.com/paper/2001.03187/) (2020)
- [Localization with Sampling-Argmax](https://mlsanity.com/paper/2110.08825/) (2021)
- [Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-constrained Optimization](https://mlsanity.com/paper/2012.07947/) (2020)
- [Interpretable Vertebral Fracture Quantification via Anchor-Free Landmarks Localization](https://mlsanity.com/paper/2204.06818/) (2022)
- [Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information](https://mlsanity.com/paper/1812.03500/) (2018)
- [Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning](https://mlsanity.com/paper/2103.07279/) (2021)
- [Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs](https://mlsanity.com/paper/2206.02087/) (2022)
- [Residual Block-based Multi-Label Classification and Localization Network with Integral Regression for Vertebrae Labeling](https://mlsanity.com/paper/2001.00170/) (2020)
- [Think about boundary: Fusing multi-level boundary information for landmark heatmap regression](https://mlsanity.com/paper/2008.10924/) (2020)

## Abstract (arXiv)

Developmental Canal Stenosis (DCS) quantification is crucial in cervical spondylosis screening. Compared with quantifying DCS manually, a more efficient and time-saving manner is provided by deep keypoint localization networks, which can be implemented in either the coordinate or the image domain. However, the vertebral visualization features often lead to abnormal topological structures during keypoint localization, including keypoint distortion with edges and weakly connected structures, which cannot be fully suppressed in either the coordinate or image domain alone. To overcome this limitation, a keypoint-edge and a reparameterization modules are utilized to restrict these abnormal structures in a cross-domain manner. The keypoint-edge constraint module restricts the keypoints on the edges of vertebrae, which ensures that the distribution pattern of keypoint coordinates is consistent with those for DCS quantification. And the reparameterization module constrains the weakly connected structures in image-domain heatmaps with coordinates combined. Moreover, the cross-domain network improves spatial generalization by utilizing heatmaps and incorporating coordinates for accurate localization, which avoids the trade-off between these two properties in an individual domain. Comprehensive results of distinct quantification tasks show the superiority and generability of the proposed Topology-inspired Cross-domain Network (TCN) compared with other competing localization methods.