mlsanityData to 7 Oct 2026

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

arXiv 2109.02722 ↗First posted 6 Sep 2021cs.CVcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 12 of its 48 references that are in the map. First posted on arXiv 6 Sep 2021.

Builds on

12 of 48 references.

References · 12

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  7. 2020SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Imagescited by 66
  8. 2018Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registrationcited by 23
  9. 2018X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgerycited by 22
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  11. 2018Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imagingcited by 4
  12. 2020An End-to-end Deep Learning Approach for Landmark Detection and Matching in Medical Imagescited by 3

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Cited by year

Citing papers per year: 2024: 1; 2025: 3.

4 papers.

  1. 2025landmarker: a Toolkit for Anatomical Landmark Localization in 2D/3D Imagescited by 3
  2. 2025A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registrationcited by 3
  3. 2025Towards Markerless Intraoperative Tracking of Deformable Spine Tissuecited by 1
  4. 2024Adaptive Query Prompting for Multi-Domain Landmark Detectioncited by 0