mlsanityData to 7 Oct 2026

Clustering Propagation for Universal Medical Image Segmentation

arXiv 2403.16646 ↗First posted 25 Mar 2024cs.CVcited by 11

Not on the map. Why?

Abstract (arXiv)

Prominent solutions for medical image segmentation are typically tailored for automatic or interactive setups, posing challenges in facilitating progress achieved in one task to another.! This! also! necessitates! separate! models for each task, duplicating both training time and parameters.! To! address! above! issues,! we! introduce! S2VNet,! a! universal! framework! that! leverages! Slice-to-Volume! propagation! to! unify automatic/interactive segmentation within a single model and one training session. Inspired by clustering-based segmentation techniques, S2VNet makes full use of the slice-wise structure of volumetric data by initializing cluster centers from the cluster! results! of! previous! slice.! This enables knowledge acquired from prior slices to assist in the segmentation of the current slice, further efficiently bridging the communication between remote slices using mere 2D networks. Moreover, such a framework readily accommodates interactive segmentation with no architectural change, simply by initializing centroids from user inputs. S2VNet distinguishes itself by swift inference speeds and reduced memory consumption compared to prevailing 3D solutions. It can also handle multi-class interactions with each of them serving to initialize different centroids. Experiments on three benchmarks demonstrate S2VNet surpasses task-specified solutions on both automatic/interactive setups.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 82 of its 119 references that are in the map. First posted on arXiv 25 Mar 2024.

Builds on

82 of 119 references.

References · 82

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  3. 2020End-to-End Object Detection with Transformerscited by 7,384
  4. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  5. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,338
  6. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  7. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  8. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  9. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  10. 2021Per-Pixel Classification is Not All You Need for Semantic Segmentationcited by 1,148
  11. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  12. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
38 more
  1. 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 583
  2. 2022AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentationcited by 347
  3. 2020UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentationcited by 331
  4. 2021Medical Transformer: Gated Axial-Attention for Medical Image Segmentationcited by 319
  5. 2018Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptationcited by 295
  6. 2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
  7. 2016Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentationcited by 263
  8. 2021Exploring Cross-Image Pixel Contrast for Semantic Segmentationcited by 242
  9. 2021CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentationcited by 227
  10. 2019CE-Net: Context Encoder Network for 2D Medical Image Segmentationcited by 219
  11. 2023Segment and Track Anythingcited by 193
  12. 2022UNeXt: MLP-based Rapid Medical Image Segmentation Networkcited by 185
  13. 2020Medical Image Segmentation Using Deep Learning: A Surveycited by 163
  14. 2016Deep Interactive Object Selectioncited by 160
  15. 2019A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysiscited by 150
  16. 2022Rethinking Semantic Segmentation: A Prototype Viewcited by 140
  17. 2018ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentationcited by 133
  18. 2021UTNet: A Hybrid Transformer Architecture for Medical Image Segmentationcited by 116
  19. 2017DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentationcited by 110
  20. 2017Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuningcited by 110
  21. 20223D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentationcited by 94
  22. 2019Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidthcited by 88
  23. 2022GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Modelscited by 87
  24. 2022Deep Hierarchical Semantic Segmentationcited by 84
  25. 2016DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networkscited by 79
  26. 2020CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentationcited by 79
  27. 2024OMG-Seg: Is One Model Good Enough For All Segmentation?cited by 73
  28. 2020Adversarial Style Mining for One-Shot Unsupervised Domain Adaptationcited by 64
  29. 2022CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentationcited by 60
  30. 2017Recurrent Pixel Embedding for Instance Groupingcited by 57
  31. 2021MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentationcited by 53
  32. 2020Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistencycited by 52
  33. 2022A Generalized Framework for Video Instance Segmentationcited by 52
  34. 2019C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentationcited by 49
  35. 2018Abdominal multi-organ segmentation with organ-attention networks and statistical fusioncited by 46
  36. 2020NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Imagescited by 46
  37. 2021MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learningcited by 45
  38. 2019Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learningcited by 41

50 most cited of 82 All 82 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 6; 2026: 2.

11 papers.

  1. 2024MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAMcited by 17
  2. 2025Learning Clustering-based Prototypes for Compositional Zero-shot Learningcited by 13
  3. 2024Clustering for Protein Representation Learningcited by 6
  4. 2024General and Task-Oriented Video Segmentationcited by 6
  5. 2025X-GAN: A Generative AI-Powered Unsupervised Model for Main Vessel Segmentation of Glaucoma Screeningcited by 2
  6. 2025Adaptive Spatial Augmentation for Semi-supervised Semantic Segmentationcited by 1
  7. 2025BioVessel-Net and RetinaMix: Unsupervised Retinal Vessel Segmentation from OCTA Imagescited by 1
  8. 2025ClusterStyle: Modeling Intra-Style Diversity with Prototypical Clustering for Stylized Motion Generationcited by 1
  9. 2026SPEGC: Continual Test-Time Adaptation via Semantic-Prompt-Enhanced Graph Clustering for Medical Image Segmentationcited by 1
  10. 2025Data Skeleton Learning: Scalable Active Clustering with Sparse Graph Structurescited by 0
  11. 2026Hyperbolic Hierarchical Clustering for Visual Representation Learningcited by 0