mlsanityData to 7 Oct 2026

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

arXiv 1606.04797 ↗First posted 15 Jun 2016cs.CVcited by 2,284

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Cited by 41 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes. In this work we propose an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network. Our CNN is trained end-to-end on MRI volumes depicting prostate, and learns to predict segmentation for the whole volume at once. We introduce a novel objective function, that we optimise during training, based on Dice coefficient. In this way we can deal with situations where there is a strong imbalance between the number of foreground and background voxels. To cope with the limited number of annotated volumes available for training, we augment the data applying random non-linear transformations and histogram matching. We show in our experimental evaluation that our approach achieves good performances on challenging test data while requiring only a fraction of the processing time needed by other previous methods.

Key facts. Cited by 2,284 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jun 2016.

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Citing papers per year: 2021: 352; 2022: 388; 2023: 419; 2024: 403; 2025: 394; 2026: 328.

2,284 papers.

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  1. 2023Segment Anythingcited by 8,158
  2. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,338
  3. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  4. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  5. 2021Per-Pixel Classification is Not All You Need for Semantic Segmentationcited by 1,148
  6. 2022Planning-oriented Autonomous Drivingcited by 1,068
  7. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  8. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 823
  9. 2021MDETR -- Modulated Detection for End-to-End Multi-Modal Understandingcited by 811
  10. 2024Structured 3D Latents for Scalable and Versatile 3D Generationcited by 742
  11. 2021The Medical Segmentation Decathloncited by 620
  12. 2021Scaling Open-Vocabulary Image Segmentation with Image-Level Labelscited by 471
38 more
  1. 2022AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentationcited by 347
  2. 2023Faster Segment Anything: Towards Lightweight SAM for Mobile Applicationscited by 345
  3. 2021Medical Transformer: Gated Axial-Attention for Medical Image Segmentationcited by 319
  4. 2024VM-UNet: Vision Mamba UNet for Medical Image Segmentationcited by 286
  5. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  6. 2021TransBTS: Multimodal Brain Tumor Segmentation Using Transformercited by 265
  7. 2021LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentationcited by 259
  8. 2022Transformers in Medical Imaging: A Surveycited by 245
  9. 2021SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retrainingcited by 235
  10. 2023Segment Anything in Medical Imagescited by 226
  11. 2021K-Net: Towards Unified Image Segmentationcited by 211
  12. 2021Decoupling Zero-Shot Semantic Segmentationcited by 198
  13. 2021UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformercited by 197
  14. 2022RTMDet: An Empirical Study of Designing Real-Time Object Detectorscited by 192
  15. 2022Medical Image Segmentation Review: The success of U-Netcited by 190
  16. 2023Universal Instance Perception as Object Discovery and Retrievalcited by 190
  17. 2022UNeXt: MLP-based Rapid Medical Image Segmentation Networkcited by 185
  18. 2022Language as Queries for Referring Video Object Segmentationcited by 180
  19. 2022ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentationcited by 178
  20. 2024U-KAN Makes Strong Backbone for Medical Image Segmentation and Generationcited by 176
  21. 2023AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Modelscited by 169
  22. 2021Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformerscited by 159
  23. 2021Instances as Queriescited by 157
  24. 2021Referring Transformer: A One-step Approach to Multi-task Visual Groundingcited by 143
  25. 2022SeqTR: A Simple yet Universal Network for Visual Groundingcited by 138
  26. 2021End-to-End Referring Video Object Segmentation with Multimodal Transformerscited by 135
  27. 2021Few-Shot Segmentation via Cycle-Consistent Transformercited by 133
  28. 2022TruFor: Leveraging all-round clues for trustworthy image forgery detection and localizationcited by 130
  29. 2023APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot ADcited by 128
  30. 2022SynthStrip: Skull-Stripping for Any Brain Imagecited by 126
  31. 2024OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understandingcited by 126
  32. 2024AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detectioncited by 126
  33. 2023Transformer-Based Visual Segmentation: A Surveycited by 124
  34. 2023SAM-Med2Dcited by 123
  35. 2024VISA: Reasoning Video Object Segmentation via Large Language Modelscited by 123
  36. 2023DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Modelscited by 122
  37. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 121
  38. 2022Realistic One-shot Mesh-based Head Avatarscited by 119

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