In this work we present a novel approach for unsupervised multi-graph matching, which applies to problems for which a Gaussian distribution of keypoint features can be assumed. We leverage cycle consistency as loss for self-supervised learning, and determine Gaussian parameters through Bayesian Optimization, yielding a highly efficient approach that scales to large datasets. Our fully unsupervised approach enables us to reach the accuracy of state-of-the-art supervised methodology for the biomedical use case of semantic cell annotation in 3D microscopy images of the worm C. elegans. To this end, our approach yields the first unsupervised atlas of C. elegans, i.e. a model of the joint distribution of all of its cell nuclei, without the need for any ground truth cell annotation. This advancement enables highly efficient semantic annotation of cells in large microscopy datasets, overcoming a current key bottleneck. Beyond C. elegans, our approach offers fully unsupervised construction of cell-level atlases for any model organism with a stereotyped body plan down to the level of unique semantic cell labels, and thus bears the potential to catalyze respective biomedical studies in a range of further species.

翻译：本文提出了一种新颖的无监督多图匹配方法，适用于关键点特征服从高斯分布的问题。我们利用循环一致性作为自监督学习的损失函数，并通过贝叶斯优化确定高斯参数，从而构建了一种可扩展至大规模数据集的高效方法。在秀丽隐杆线虫三维显微图像语义细胞标注的生物医学应用案例中，我们完全无监督的方法达到了当前最先进监督方法的精度水平。由此，我们的方法首次构建了秀丽隐杆线虫的无监督细胞图谱——即其所有细胞核联合分布的模型，且无需任何真实细胞标注数据。这一进展实现了对大规模显微数据集中细胞的高效语义标注，突破了当前的关键技术瓶颈。除秀丽隐杆线虫外，本方法能够为任何具有固定体式结构（直至独特语义细胞标签级别）的模式生物构建完全无监督的细胞级图谱，因此具备推动跨物种生物医学研究的潜力。