  Segmenting medical images is critical to facilitating both patient diagnoses
and quantitative research. A major limiting factor is the lack of labeled data,
as obtaining expert annotations for each new set of imaging data and task can
be labor intensive and inconsistent among annotators. We present CUTS, an
unsupervised deep learning framework for medical image segmentation. CUTS
operates in two stages. For each image, it produces an embedding map via
intra-image contrastive learning and local patch reconstruction. Then, these
embeddings are partitioned at dynamic granularity levels that correspond to the
data topology. CUTS yields a series of coarse-to-fine-grained segmentations
that highlight features at various granularities. We applied CUTS to retinal
fundus images and two types of brain MRI images to delineate structures and
patterns at different scales. When evaluated against predefined anatomical
masks, CUTS improved the dice coefficient and Hausdorff distance by at least
10% compared to existing unsupervised methods. Finally, CUTS showed performance
on par with Segment Anything Models (SAM, MedSAM, SAM-Med2D) pre-trained on
gigantic labeled datasets.
