mlsanityData to 14 Oct 2026

Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An approach for the crossMoDA Challenge

arXiv 2110.01607 ↗First posted 2 Oct 2021eess.IV cs.CVcited by 7

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Abstract (arXiv)

The purpose of this study is to apply and evaluate out-of-the-box deep learning frameworks for the crossMoDA challenge. We use the CUT model, a model for unpaired image-to-image translation based on patchwise contrastive learning and adversarial learning, for domain adaptation from contrast-enhanced T1 MR to high-resolution T2 MR. As data augmentation, we generate additional images with vestibular schwannomas with lower signal intensity. For the segmentation task, we use the nnU-Net framework. Our final submission achieved mean Dice scores of 0.8299 in the validation phase and 0.8253 in the test phase. Our method ranked 3rd in the crossMoDA challenge.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 15 of its 24 references that are in the map. First posted on arXiv 2 Oct 2021.

Builds on

15 of 24 references.

References · 15

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3 more
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Cited by year

Citing papers per year: 2022: 2; 2023: 4; 2024: 1.

7 papers.

  1. 2022CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentationcited by 58
  2. 2022Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive Learningcited by 5
  3. 2023Medical Image Segmentation with Domain Adaptation: A Surveycited by 5
  4. 2023MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentationcited by 4
  5. 2024CT-based brain ventricle segmentation via diffusion Schr"odinger Bridge without target domain ground truthscited by 2
  6. 2023Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentationcited by 1
  7. 2023Fine-Grained Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentationcited by 1