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Current deep-learning-based registration algorithms often exploitintensity-based similarity measures as the loss function, where densecorrespondence between a pair of moving and fixed images is optimized throughbackpropagation during training. However, intensity-based metrics can bemisleading when the assumption of intensity class correspondence is violated,especially in cross-modality or contrast-enhanced images. Moreover, existinglearning-based registration methods are predominantly applicable to pairwiseregistration and are rarely extended to groupwise registration or simultaneousregistration with multiple images. In this paper, we propose a new imageregistration framework based on multivariate mixture model (MvMM) and neuralnetwork estimation. A generative model consolidating both appearance andanatomical information is established to derive a novel loss function capableof implementing groupwise registration. We highlight the versatility of theproposed framework for various applications on multimodal cardiac images,including single-atlas-based segmentation (SAS) via pairwise registration andmulti-atlas segmentation (MAS) unified by groupwise registration. We evaluatedperformance on two publicly available datasets, i.e. MM-WHS-2017 andMS-CMRSeg-2019. The results show that the proposed framework achieved anaverage Dice score of $0.871\pm 0.025$ for whole-heart segmentation on MRimages and $0.783\pm 0.082$ for myocardium segmentation on LGE MR images.

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