Fully Unsupervised Annotation of C. Elegans
Journal:
arXiv
Published Date:
Mar 10, 2025
Abstract
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 use case of annotating cell
nuclei 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 annotation of
cell nuclei in large microscopy datasets of C. elegans. Beyond C. elegans, our
approach offers fully unsupervised construction of cell-level atlases for any
model organism with a stereotyped cell lineage, and thus bears the potential to
catalyze respective comparative developmental studies in a range of further
species.
