# A 3D Multi-Style Cross-Modality Segmentation Framework for Segmenting Vestibular Schwannoma and Cochlea

arXiv: https://arxiv.org/abs/2311.11578 · HTML: https://mlsanity.com/paper/2311.11578/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 20 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation](https://mlsanity.com/paper/2201.02831/) (2022)
- [Modulated Contrast for Versatile Image Synthesis](https://mlsanity.com/paper/2203.09333/) (2022)
- [Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive Learning](https://mlsanity.com/paper/2210.04255/) (2022)
- [Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentation](https://mlsanity.com/paper/2303.14998/) (2023)
- [Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma](https://mlsanity.com/paper/2208.04680/) (2022)

## Built on by (most cited first, 1 of 1)

- [IntraStyler: Intra-Domain Style Synthesis for Cross-Modality MRI Domain Adaptation](https://mlsanity.com/paper/2601.00212/) (2026)

## Abstract (arXiv)

The crossMoDA2023 challenge aims to segment the vestibular schwannoma (sub-divided into intra- and extra-meatal components) and cochlea regions of unlabeled hrT2 scans by leveraging labeled ceT1 scans. In this work, we proposed a 3D multi-style cross-modality segmentation framework for the crossMoDA2023 challenge, including the multi-style translation and self-training segmentation phases. Considering heterogeneous distributions and various image sizes in multi-institutional scans, we first utilize the min-max normalization, voxel size resampling, and center cropping to obtain fixed-size sub-volumes from ceT1 and hrT2 scans for training. Then, we perform the multi-style image translation phase to overcome the intensity distribution discrepancy between unpaired multi-modal scans. Specifically, we design three different translation networks with 2D or 2.5D inputs to generate multi-style and realistic target-like volumes from labeled ceT1 volumes. Finally, we perform the self-training volumetric segmentation phase in the target domain, which employs the nnU-Net framework and iterative self-training method using pseudo-labels for training accurate segmentation models in the unlabeled target domain. On the crossMoDA2023 validation dataset, our method produces promising results and achieves the mean DSC values of 72.78% and 80.64% and ASSD values of 5.85 mm and 0.25 mm for VS tumor and cochlea regions, respectively. Moreover, for intra- and extra-meatal regions, our method achieves the DSC values of 59.77% and 77.14%, respectively.