# MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation

arXiv: https://arxiv.org/abs/2006.15573 · HTML: https://mlsanity.com/paper/2006.15573/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [A persistent homology-based topological loss for CNN-based multi-class segmentation of CMR](https://mlsanity.com/paper/2107.12689/) (2021)
- [Multi-Modality Cardiac Image Computing: A Survey](https://mlsanity.com/paper/2208.12881/) (2022)
- [Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion](https://mlsanity.com/paper/2202.02000/) (2022)
- [Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation](https://mlsanity.com/paper/2302.03537/) (2023)
- [Multi-class probabilistic atlas-based whole heart segmentation method in cardiac CT and MRI](https://mlsanity.com/paper/2102.01822/) (2021)
- [MGAug: Multimodal Geometric Augmentation in Latent Spaces of Image Deformations](https://mlsanity.com/paper/2312.13440/) (2023)
- [BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement](https://mlsanity.com/paper/2206.02377/) (2022)
- [Unsupervised Multi-Modality Registration Network based on Spatially Encoded Gradient Information](https://mlsanity.com/paper/2105.07392/) (2021)
- [Bayesian Unsupervised Disentanglement of Anatomy and Geometry for Deep Groupwise Image Registration](https://mlsanity.com/paper/2401.02141/) (2024)
- [A low-rank representation for unsupervised registration of medical images](https://mlsanity.com/paper/2105.09548/) (2021)

## Abstract (arXiv)

Current deep-learning-based registration algorithms often exploit intensity-based similarity measures as the loss function, where dense correspondence between a pair of moving and fixed images is optimized through backpropagation during training. However, intensity-based metrics can be misleading when the assumption of intensity class correspondence is violated, especially in cross-modality or contrast-enhanced images. Moreover, existing learning-based registration methods are predominantly applicable to pairwise registration and are rarely extended to groupwise registration or simultaneous registration with multiple images. In this paper, we propose a new image registration framework based on multivariate mixture model (MvMM) and neural network estimation. A generative model consolidating both appearance and anatomical information is established to derive a novel loss function capable of implementing groupwise registration. We highlight the versatility of the proposed framework for various applications on multimodal cardiac images, including single-atlas-based segmentation (SAS) via pairwise registration and multi-atlas segmentation (MAS) unified by groupwise registration. We evaluated performance on two publicly available datasets, i.e. MM-WHS-2017 and MS-CMRSeg-2019. The results show that the proposed framework achieved an average Dice score of 0.871± 0.025 for whole-heart segmentation on MR images and 0.783± 0.082 for myocardium segmentation on LGE MR images.