Some new MICCAI papers

Congratulations to Boqi Chen, Hastings Greer, Lin Tian and Nurislam Tursynbek who got their MICCAI papers accepted. These papers explore image synthesis via retrieval [bc23] , image registration via deep learning where inverse consistency is assured even when using multiple registration steps [hg23] , and unsupervised image segmentation using diffusion models [nt23] .



MRIS: A Multi-modal Retrieval Approach for Image Synthesis on Diverse Modalities

Multiple imaging modalities are often used for disease diagnosis, prediction, or population-based analyses. However, not all modalities might be available due to cost, different study designs, or changes in imaging technology. If the differences between the types of imaging are small, data harmonization approaches can be used; for larger changes, direct image synthesis approaches have been explored. In this paper, we develop an approach based on multi-modal metric learning to synthesize images of diverse modalities. We use metric learning via multi-modal image retrieval, resulting in embeddings that can relate images of different modalities. Given a large image database, the learned image embeddings allow us to use k-nearest neighbor (k-NN) regression for image synthesis. Our driving medical problem is knee osteoarthritis (KOA), but our developed method is general after proper image alignment. We test our approach by synthesizing cartilage thickness maps obtained from 3D magnetic resonance (MR) images using 2D radiographs. Our experiments show that the proposed method outperforms direct image synthesis and that the synthesized thickness maps retain information relevant to downstream tasks such as progression prediction and Kellgren-Lawrence grading (KLG). Our results suggest that retrieval approaches can be used to obtain high-quality and meaningful image synthesis results given large image databases.


UNC Biomedical Image Analysis Group (UNC-biag)

UNC Biomedical Image Analysis Group (unc-biag)