# Enhancing Label-Driven Deep Deformable Image Registration with Local Distance Metrics for State-of-the-Art Cardiac Motion Tracking

arXiv: https://arxiv.org/abs/1812.01859 · HTML: https://mlsanity.com/paper/1812.01859/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2018.

## Built on by (most cited first, 7 of 7)

- [Learning the Effect of Registration Hyperparameters with HyperMorph](https://mlsanity.com/paper/2203.16680/) (2022)
- [Cross-Modality Image Registration using a Training-Time Privileged Third Modality](https://mlsanity.com/paper/2207.12901/) (2022)
- [MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network](https://mlsanity.com/paper/2111.12123/) (2021)
- [Structure-aware registration network for liver DCE-CT images](https://mlsanity.com/paper/2303.04595/) (2023)
- [Semi-weakly-supervised neural network training for medical image registration](https://mlsanity.com/paper/2402.10728/) (2024)
- [MBSS-T1: Model-Based Subject-Specific Self-Supervised Motion Correction for Robust Cardiac T1 Mapping](https://mlsanity.com/paper/2408.11992/) (2024)
- [Deep End-to-end Adaptive k-Space Sampling, Reconstruction, and Registration for Dynamic MRI](https://mlsanity.com/paper/2411.18249/) (2024)

## Abstract (arXiv)

While deep learning has achieved significant advances in accuracy for medical image segmentation, its benefits for deformable image registration have so far remained limited to reduced computation times. Previous work has either focused on replacing the iterative optimization of distance and smoothness terms with CNN-layers or using supervised approaches driven by labels. Our method is the first to combine the complementary strengths of global semantic information (represented by segmentation labels) and local distance metrics that help align surrounding structures. We demonstrate significant higher Dice scores (of 86.5%) for deformable cardiac image registration compared to classic registration (79.0%) as well as label-driven deep learning frameworks (83.4%).