ArticleIEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision
Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network.
Article in IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2.IEEE transactions on medical imaging · 2026Article
- DescriptorMedSAM: language-image fusion with multi-aspect text guidance for medical image segmentation.Scientific reports · 2026Article
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Authors and funding
5 authors.
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Abstract
Deep learning (DL) methods have shown remarkable successes in medical image segmentation, often using large amounts of annotated data for model training. However, acquiring a large number of diverse labeled 3D medical image datasets is highly difficult and expensive. Recently, mask propagation DL methods were developed to reduce the annotation burden on 3D medical images. For example, Sli2Vol [59] proposed a self-supervised framework (SSF) to learn correspondences by matching neighboring slices via slice reconstruction in the training stage; the learned correspondences were then used to propagate a labeled slice to other slices in the test stage. But, these methods are still prone to error accumulation due to the inter-slice propagation of reconstruction errors. Also, they do not handle discontinuities well, which can occur between consecutive slices in 3D images, as they emphasize exploiting object continuity. To address these challenges, in this work, we propose a new SSF, called
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