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Cited by 195Medical Image Segmentation TechniquesDomain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

UniverSeg: Universal Medical Image Segmentation

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Butoi, Victor Ion, Gonzalez Ortiz, Jose Javier, Ma, Tianyu, Sabuncu, Mert R., Guttag, John, Dalca, Adrian V.·2023·2023 IEEE/CVF International Conference on Computer Vision (ICCV)
DOI 10.1109/iccv51070.2023.01960
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Abstract

While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies, image modalities, or labels. Given a new segmentation task, researchers generally have to train or fine-tune models. This is time-consuming and poses a substantial barrier for clinical researchers, who often lack the resources and expertise to train neural networks.We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training. Given a query image and an example set of image-label pairs that define a new segmentation task, UniverSeg employs a new CrossBlock mechanism to produce accurate segmentation maps without additional training. To achieve generalization to new tasks, we have gathered and standardized a collection of 53 open-access medical segmentation datasets with over 22,000 scans, which we refer to as MegaMedical. We used this collection to train UniverSeg on a diverse set of anatomies and imaging modalities. We demonstrate that UniverSeg substantially outperforms several related methods on unseen tasks, and thoroughly analyze and draw insights about important aspects of the proposed system. The UniverSeg source code and model weights are freely available at https://universeg.csail.mit.edu

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  • In-context learning models are computationally efficient and can adapt their predictions based on a small support set.

    “Unlike previous approaches, that rely on training CNNs [29] or computationally expensive atlas-based registration methods as reverse classifiers [5], [28], in-context learning models are computationally efficient and can adapt their predictions based on a small support set.”

    From ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality (2025) · cites this paper as [10]

  • UniverSeg was trained on 53 open-access medical segmentation datasets called MegaMedical, covering a wide range of image modalities and structures.

    “It was trained on a collection of 53 open-access medical segmentation datasets called MegaMedical, covering a wide range of image modalities and structures.”

    From ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality (2025) · cites this paper as [10]

  • UniverSeg is a UNet-like architecture trained on MegaMedical and designed to generalize to unseen medical segmentation tasks without additional training.

    “UniverSeg : a UNet-like architecture trained on a large collection of medical segmentation datasets (MegaMedical) and designed to generalize to unseen medical segmentation tasks without additional training.”

    From ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality (2025) · cites this paper as [10]

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Cited by

Sorted by citations

Authors
Butoi, V. I., Gonzalez Ortiz, J. J., Ma, T., Sabuncu, M. R., Guttag, J., & Dalca, A. V.
Affiliations
  • MIT Computer Science and Artificial Intelligence Laboratory (US)
  • MIT CSAIL
  • Cornell University (US)
ORCID
Venue
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher
IEEE
Published
2023-10-01
Pages
21381-21394
DOI
10.1109/iccv51070.2023.01960
OpenAlex
W4390873519
Cited by
195
References
7
Citations by year
2026: 55 · 2025: 86 · 2024: 45 · 2023: 9
FWCI
20.85
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
medical image segmentation, zero-shot segmentation, cross-modal generalization

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