mlsanityData to 7 Oct 2026

CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation

arXiv 2201.02831 ↗First posted 8 Jan 2022eess.IV cs.CVcited by 58

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Abstract (arXiv)

Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While a large variety of DA techniques has been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large and multi-class benchmark for unsupervised cross-modality DA. The challenge's goal is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are performed using contrast-enhanced T1 (ceT1) MRI. However, there is growing interest in using non-contrast sequences such as high-resolution T2 (hrT2) MRI. Therefore, we created an unsupervised cross-modality segmentation benchmark. The training set provides annotated ceT1 (N=105) and unpaired non-annotated hrT2 (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137). A total of 16 teams submitted their algorithm for the evaluation phase. The level of performance reached by the top-performing teams is strikingly high (best median Dice - VS:88.4%; Cochleas:85.7%) and close to full supervision (median Dice - VS:92.5%; Cochleas:87.7%). All top-performing methods made use of an image-to-image translation approach to transform the source-domain images into pseudo-target-domain images. A segmentation network was then trained using these generated images and the manual annotations provided for the source image.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). Builds on 40 of its 86 references that are in the map. First posted on arXiv 8 Jan 2022.

Builds on

40 of 86 references.

References · 40

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  25. 2021Self-Training Based Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentationcited by 5
  26. 2021Unsupervised Cross-Modality Domain Adaptation for Segmenting Vestibular Schwannoma and Cochlea with Data Augmentation and Model Ensemblecited by 5
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 9; 2023: 25; 2024: 8; 2025: 11; 2026: 5.

58 papers.

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  1. 2023Large-Vocabulary Segmentation for Medical Images with Text Promptscited by 60
  2. 2023SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million maskscited by 54
  3. 2023The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningiomacited by 51
  4. 2024Revisiting MAE pre-training for 3D medical image segmentationcited by 33
  5. 2023MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Visioncited by 31
  6. 2023SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinomacited by 28
  7. 2023UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentationcited by 21
  8. 2022COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Trainingcited by 20
  9. 2022medigan: a Python library of pretrained generative models for medical image synthesiscited by 14
  10. 2023Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representationscited by 13
  11. 2023Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRIcited by 11
  12. 2023SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentationcited by 10
38 more
  1. 2024FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentationcited by 10
  2. 2025Revisiting 2D Foundation Models for Scalable 3D Medical Image Classificationcited by 9
  3. 2024Unified Cross-Modal Medical Image Synthesis with Hierarchical Mixture of Product-of-Expertscited by 8
  4. 2024Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasoundcited by 7
  5. 2023Cross-modal tumor segmentation using generative blending augmentation and self trainingcited by 6
  6. 2024LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantificationcited by 6
  7. 2024Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networkscited by 6
  8. 2022Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusioncited by 5
  9. 2022Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directionscited by 5
  10. 2022Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive Learningcited by 5
  11. 2023Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentationcited by 5
  12. 2023Structure Preserving Cycle-GAN for Unsupervised Medical Image Domain Adaptationcited by 5
  13. 2023Medical Image Segmentation with Domain Adaptation: A Surveycited by 5
  14. 2022Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannomacited by 4
  15. 2022Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentationcited by 4
  16. 2023MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentationcited by 4
  17. 2023Learning Site-specific Styles for Multi-institutional Unsupervised Cross-modality Domain Adaptationcited by 3
  18. 2025Single Image Test-Time Adaptation via Multi-View Co-Trainingcited by 3
  19. 2025Is Visual in-Context Learning for Compositional Medical Tasks within Reach?cited by 3
  20. 2023Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translationcited by 2
  21. 2025Multi-Modal Explainable Medical AI Assistant for Trustworthy Human-AI Collaborationcited by 2
  22. 2025MedROV: Towards Real-Time Open-Vocabulary Detection Across Diverse Medical Imaging Modalitiescited by 2
  23. 2026Contrast-X: A Multi-Modal Contrast Image Synthesis Benchmark and Universal Modality Flow Matchingcited by 2
  24. 2026The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Predictioncited by 2
  25. 2022An Unpaired Cross-modality Segmentation Framework Using Data Augmentation and Hybrid Convolutional Networks for Segmenting Vestibular Schwannoma and Cochleacited by 1
  26. 2022M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient Supervisioncited by 1
  27. 2023Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentationcited by 1
  28. 2023BugNIST -- a Large Volumetric Dataset for Object Detection under Domain Shiftcited by 1
  29. 2023OnUVS: Online Feature Decoupling Framework for High-Fidelity Ultrasound Video Synthesiscited by 1
  30. 2023A 3D Multi-Style Cross-Modality Segmentation Framework for Segmenting Vestibular Schwannoma and Cochleacited by 1
  31. 2023Fine-Grained Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentationcited by 1
  32. 2024Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Datacited by 1
  33. 2025Unified 3D MRI Representations via Sequence-Invariant Contrastive Learningcited by 1
  34. 2025M3DA: Benchmark for Unsupervised Domain Adaptation in 3D Medical Image Segmentationcited by 1
  35. 2025Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challengecited by 1
  36. 2025crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023cited by 1
  37. 2025SAMRI: Segment Any MRIcited by 1
  38. 2026A3-TTA: Adaptive Anchor Alignment Test-Time Adaptation for Image Segmentationcited by 1

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