Abstract
Medical image segmentation remains fragmented along two axes: segmentation paradigms and data dimensionality. Existing methods are typically developed separately for semantic, in-context, and interactive segmentation, and are further specialized to either native 2D images or 3D volumetric data. In clinical practice, however, segmentation workflows take many forms: a case may be initialized by semantic prediction, reference-guided segmentation, or user interaction. Regardless of how it begins, fine-grained refinement is naturally performed on 2D views; for volumetric scans, such 2D edits must propagate coherently to the rest of the volume. We present UniPro, a unified model that bridges segmentation paradigms and data dimensionality, using propagation to extend 2D segmentation to 3D volumes. Our key insight is that volumetric propagation and in-context segmentation share the same reference-conditioned prediction mechanism, differing only in whether the reference image-mask pairs come from other cases or from previously segmented neighboring slices. Building on this view, UniPro supports semantic, in-context, interactive, and propagation-based segmentation within a single slice-based framework, using class priors, reference exemplars, user clicks, and neighboring-slice predictions as mode-specific conditioning inputs. To improve propagation reliability, UniPro further incorporates bidirectional and 3D supervision to regularize slice-wise propagation beyond per-slice losses. Extensive experiments across diverse modalities and anatomies show that UniPro achieves strong performance across all segmentation settings, enabling annotation-efficient 3D segmentation from sparse 2D initialization and reducing slice-by-slice correction effort.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Guo, B., Gao, Y., Ye, M., Zhou, Y., Gu, D., Zhang, G., Axel, L., & Metaxas, D. (2026). UniPro: Unified Multi-Mode Medical Image Segmentation from 2D Images to 3D Volumes via Propagation. https://omanscience.com/en/articles/unipro-unified-multi-mode-medical-image-segmentation-from-2d-images-to-3d-volumes-via-propagation
MLA 9
Guo, Bangwei, et al. "UniPro: Unified Multi-Mode Medical Image Segmentation from 2D Images to 3D Volumes via Propagation." https://omanscience.com/en/articles/unipro-unified-multi-mode-medical-image-segmentation-from-2d-images-to-3d-volumes-via-propagation.
Chicago (author–date)
Guo, Bangwei, Yunhe Gao, Meng Ye, Yang Zhou, Difei Gu, Guoning Zhang, Leon Axel, and Dimitris Metaxas. 2026. "UniPro: Unified Multi-Mode Medical Image Segmentation from 2D Images to 3D Volumes via Propagation." https://omanscience.com/en/articles/unipro-unified-multi-mode-medical-image-segmentation-from-2d-images-to-3d-volumes-via-propagation.
Harvard
Guo, B., Gao, Y., Ye, M., Zhou, Y., Gu, D., Zhang, G., Axel, L. and Metaxas, D. (2026) 'UniPro: Unified Multi-Mode Medical Image Segmentation from 2D Images to 3D Volumes via Propagation', Available at: https://omanscience.com/en/articles/unipro-unified-multi-mode-medical-image-segmentation-from-2d-images-to-3d-volumes-via-propagation.
Vancouver
Guo B, Gao Y, Ye M, Zhou Y, Gu D, Zhang G, et al. UniPro: Unified Multi-Mode Medical Image Segmentation from 2D Images to 3D Volumes via Propagation. https://omanscience.com/en/articles/unipro-unified-multi-mode-medical-image-segmentation-from-2d-images-to-3d-volumes-via-propagation
IEEE
B. Guo, Y. Gao, M. Ye, Y. Zhou, D. Gu, G. Zhang, L. Axel, and D. Metaxas, "UniPro: Unified Multi-Mode Medical Image Segmentation from 2D Images to 3D Volumes via Propagation," https://omanscience.com/en/articles/unipro-unified-multi-mode-medical-image-segmentation-from-2d-images-to-3d-volumes-via-propagation.