mlsanityData to 7 Oct 2026

CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation

arXiv 2209.11359 ↗First posted 23 Sep 2022cs.CVcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 25 of its 57 references that are in the map. First posted on arXiv 23 Sep 2022.

Builds on

25 of 57 references.

References · 25

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13 more
  1. 2018A Probabilistic U-Net for Segmentation of Ambiguous Imagescited by 281
  2. 2022Unsupervised Semantic Segmentation by Distilling Feature Correspondencescited by 241
  3. 2019Data augmentation using learned transformations for one-shot medical image segmentationcited by 138
  4. 2020Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotationcited by 128
  5. 2020Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clusteringcited by 61
  6. 2014Multi-Atlas Segmentation of Biomedical Images: A Surveycited by 56
  7. 2020Realistic Adversarial Data Augmentation for MR Image Segmentationcited by 44
  8. 2018Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learningcited by 36
  9. 2019Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentationcited by 33
  10. 2020LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentationcited by 28
  11. 2020FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimationcited by 21
  12. 2019Coarse Graining of Data via Inhomogeneous Diffusion Condensationcited by 9
  13. 2022Unsupervised Tissue Segmentation via Deep Constrained Gaussian Networkcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 2; 2026: 1.

8 papers.

  1. 2024ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Imagescited by 19
  2. 2024Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifoldscited by 13
  3. 2024DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Imagescited by 5
  4. 2026Dispersion Loss Counteracts Embedding Condensation and Improves Generalization in Small Language Modelscited by 2
  5. 2024Self and Mixed Supervision to Improve Training Labels for Multi-Class Medical Image Segmentationcited by 0
  6. 2024Leveraging Latents for Efficient Thermography Classification and Segmentationcited by 0
  7. 2025Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentationcited by 0
  8. 2025RNAGenScape: Property-Guided, Optimized Generation of mRNA Sequences with Manifold Langevin Dynamicscited by 0