mlsanityData to 7 Oct 2026

iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images

arXiv 2112.11325 ↗First posted 21 Dec 2021cs.CVcited by 27

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Abstract (arXiv)

We propose iSegFormer, a memory-efficient transformer that combines a Swin transformer with a lightweight multilayer perceptron (MLP) decoder. With the efficient Swin transformer blocks for hierarchical self-attention and the simple MLP decoder for aggregating both local and global attention, iSegFormer learns powerful representations while achieving high computational efficiencies. Specifically, we apply iSegFormer to interactive 3D medical image segmentation.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 34 of its 47 references that are in the map. First posted on arXiv 21 Dec 2021.

Builds on

34 of 47 references.

References · 34

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  5. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  6. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  7. 2017Non-local Neural Networkscited by 2,290
  8. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,960
  9. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  10. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,417
  11. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  12. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
22 more
  1. 2017Deep Learning for Medical Image Analysiscited by 399
  2. 2019Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentationcited by 258
  3. 2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
  4. 2021CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentationcited by 227
  5. 2021Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentationcited by 200
  6. 2020Medical Image Segmentation Using Deep Learning: A Surveycited by 163
  7. 2021Reviving Iterative Training with Mask Guidance for Interactive Segmentationcited by 162
  8. 2016Deep Interactive Object Selectioncited by 160
  9. 2021HRFormer: High-Resolution Transformer for Dense Predictioncited by 149
  10. 2021Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusioncited by 129
  11. 2021UTNet: A Hybrid Transformer Architecture for Medical Image Segmentationcited by 116
  12. 2017DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentationcited by 110
  13. 2019Large-scale interactive object segmentation with human annotatorscited by 109
  14. 2016DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networkscited by 79
  15. 2019AdaptIS: Adaptive Instance Selection Networkcited by 64
  16. 2016Inductive Bias of Deep Convolutional Networks through Pooling Geometrycited by 53
  17. 2021MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learningcited by 45
  18. 2017Deep GrabCut for Object Selectioncited by 33
  19. 2019Fast User-Guided Video Object Segmentation by Interaction-and-Propagation Networkscited by 33
  20. 2019Interactive segmentation of medical images through fully convolutional neural networkscited by 29
  21. 2020Interactive Video Object Segmentation Using Global and Local Transfer Modulescited by 26
  22. 2020Interactive Radiotherapy Target Delineation with 3D-Fused Context Propagationcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 8; 2024: 10; 2025: 4; 2026: 2.

27 papers.

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  1. 2022SimpleClick: Interactive Image Segmentation with Simple Vision Transformerscited by 109
  2. 2023ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Imagecited by 47
  3. 2023One-Prompt to Segment All Medical Imagescited by 38
  4. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  5. 2023Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomycited by 26
  6. 2024Rethinking Interactive Image Segmentation with Low Latency, High Quality, and Diverse Promptscited by 22
  7. 2023AdaptiveClick: Clicks-aware Transformer with Adaptive Focal Loss for Interactive Image Segmentationcited by 11
  8. 2024Clustering Propagation for Universal Medical Image Segmentationcited by 11
  9. 2022Recent Progress in Transformer-based Medical Image Analysiscited by 9
  10. 2024Adapting the Segment Anything Model During Usage in Novel Situationscited by 9
  11. 2023Exploring Cycle Consistency Learning in Interactive Volume Segmentationcited by 4
  12. 2024HemoSet: The First Blood Segmentation Dataset for Automation of Hemostasis Managementcited by 4
15 more
  1. 2025Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2cited by 4
  2. 2023Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Imagescited by 3
  3. 2024ConUNETR: A Conditional Transformer Network for 3D Micro-CT Embryonic Cartilage Segmentationcited by 3
  4. 2023PE-MED: Prompt Enhancement for Interactive Medical Image Segmentationcited by 2
  5. 2024Rethinking Annotator Simulation: Realistic Evaluation of Whole-Body PET Lesion Interactive Segmentation Methodscited by 2
  6. 2024Continuous Spatio-Temporal Memory Networks for 4D Cardiac Cine MRI Segmentationcited by 2
  7. 2023Leveraging Self-Supervised Vision Transformers for Segmentation-based Transfer Function Designcited by 1
  8. 2024Quantifying Knee Cartilage Shape and Lesion: From Image to Metricscited by 1
  9. 2024TraceNet: Segment one thing efficientlycited by 0
  10. 2024UniCoN: Universal Conditional Networks for Multi-Age Embryonic Cartilage Segmentation with Sparsely Annotated Datacited by 0
  11. 2025SAMRI-2: A Memory-based Model for Cartilage and Meniscus Segmentation in 3D MRIs of the Knee Jointcited by 0
  12. 2025Downstream Analysis of Foundational Medical Vision Models for Disease Progressioncited by 0
  13. 2025MOIS-SAM2: Exemplar-based Segment Anything Model 2 for multilesion interactive segmentation of neurofibromas in whole-body MRIcited by 0
  14. 2026ActiveFreq: Integrating Active Learning and Frequency Domain Analysis for Interactive Segmentationcited by 0
  15. 2026IMVS: Interactive Medical Volume Segmentation with Test-Time Adaptation - A New Method for Annotating Radiology Datasetscited by 0