# Automatic Landmarks Correspondence Detection in Medical Images with an Application to Deformable Image Registration

arXiv: https://arxiv.org/abs/2109.02722 · HTML: https://mlsanity.com/paper/2109.02722/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 12 of 12)

- [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)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Learning to Find Good Correspondences](https://mlsanity.com/paper/1711.05971/) (2017)
- [Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation](https://mlsanity.com/paper/1904.06346/) (2019)
- [End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network](https://mlsanity.com/paper/1704.06065/) (2017)
- [SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images](https://mlsanity.com/paper/2012.02383/) (2020)
- [Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration](https://mlsanity.com/paper/1807.00467/) (2018)
- [X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery](https://mlsanity.com/paper/1803.08608/) (2018)
- [Multimodal Registration of Retinal Images Using Domain-Specific Landmarks and Vessel Enhancement](https://mlsanity.com/paper/1803.00951/) (2018)
- [Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging](https://mlsanity.com/paper/1805.10737/) (2018)
- [An End-to-end Deep Learning Approach for Landmark Detection and Matching in Medical Images](https://mlsanity.com/paper/2001.07434/) (2020)

## Built on by (most cited first, 4 of 4)

- [landmarker: a Toolkit for Anatomical Landmark Localization in 2D/3D Images](https://mlsanity.com/paper/2501.10098/) (2025)
- [A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registration](https://mlsanity.com/paper/2507.18551/) (2025)
- [Towards Markerless Intraoperative Tracking of Deformable Spine Tissue](https://mlsanity.com/paper/2506.23657/) (2025)
- [Adaptive Query Prompting for Multi-Domain Landmark Detection](https://mlsanity.com/paper/2404.01194/) (2024)

## Abstract (arXiv)

Purpose: Deformable Image Registration (DIR) can benefit from additional guidance using corresponding landmarks in the images. However, the benefits thereof are largely understudied, especially due to the lack of automatic landmark detection methods for three-dimensional (3D) medical images. Approach: We present a Deep Convolutional Neural Network (DCNN), called DCNN-Match, that learns to predict landmark correspondences in 3D images in a self-supervised manner. We trained DCNN-Match on pairs of Computed Tomography (CT) scans containing simulated deformations. We explored five variants of DCNN-Match that use different loss functions and assessed their effect on the spatial density of predicted landmarks and the associated matching errors. We also tested DCNN-Match variants in combination with the open-source registration software Elastix to assess the impact of predicted landmarks in providing additional guidance to DIR. Results: We tested our approach on lower-abdominal CT scans from cervical cancer patients: 121 pairs containing simulated deformations and 11 pairs demonstrating clinical deformations. The results showed significant improvement in DIR performance when landmark correspondences predicted by DCNN-Match were used in the case of simulated (p = 0e0) as well as clinical deformations (p = 0.030). We also observed that the spatial density of the automatic landmarks with respect to the underlying deformation affect the extent of improvement in DIR. Finally, DCNN-Match was found to generalize to Magnetic Resonance Imaging (MRI) scans without requiring retraining, indicating easy applicability to other datasets. Conclusions: DCNN-Match learns to predict landmark correspondences in 3D medical images in a self-supervised manner, which can improve DIR performance.