---
title: CrossMoDA 2023 Challenge
url: https://www.emergentmind.com/topics/crossmoda-2023
type: topic
---

# CrossMoDA 2023 Challenge

The Cross-Modality Domain Adaptation (CrossMoDA) 2023 challenge is an international competition focused on unsupervised cross-modal segmentation of vestibular schwannoma (VS) and cochlea in magnetic resonance imaging (MRI). Organized in conjunction with the MICCAI conference series, CrossMoDA serves as a dynamic benchmark for testing domain adaptation algorithms, leveraging the extreme domain shift between contrast-enhanced T1-weighted (ceT1) and high-resolution T2-weighted (T2) sequences. Its primary motivation is to automate VS and cochlea segmentation on cost-effective routine T2 scans, improving clinical workflows and broadening access to precision otoneurosurgical planning. The 2023 edition marks a significant evolution: it incorporated multi-institutional, highly heterogeneous routine data and refined the segmentation task into intra- and extra-meatal tumour compartments, intentionally increasing the clinical and methodological challenge [2506.12006].

## 1. Multi-Institutional, Heterogeneous Dataset Design

The 2023 CrossMoDA dataset comprises 959 MRI scans partitioned across three distinct cohorts and multiple institutions (see Table 1 of [2506.12006]):

| Institution                | Source ceT1 (Train) | Target T2 (Train) | Validation | Test | MRI Protocols                                |
|----------------------------|---------------------|-------------------|------------|------|-----------------------------------------------|
| London SC-GK (UK)          | 79                  | 85                | 32         | 113  | MP-RAGE (ceT1), 3D-CISS/FIESTA (T2)          |
| Tilburg SC-GK (Netherlands)| 105                 | 105               | 32         | 134  | 3D-FFE (ceT1), 3D-TSE (T2)                   |
| UK MC-RC (10 UK Sites)     | 38 ceT1/43 T2       | 47 T2             | 15         | 45   | Varied protocols, 1.0–3.0 T, Siemens/Philips/GE/Hitachi |

The MC-RC cohort reflects real-world scan diversity, with voxel sizes spanning 0.01–1 mm³, slice thickness between 1–5 mm, and a variety of magnet field strengths and vendors. Figure 1 in [2506.12006] demonstrates pronounced inter-site variability in intensity histograms and structural representations, presenting a severe domain adaptation challenge.

## 2. Refined Segmentation Task Definition and Clinical Relevance

For 2023, the segmentation objective was set as a three-class problem on T2-weighted MRI without Koos grading:

1. Intra-meatal VS (within the internal auditory canal)
2. Extra-meatal VS (in the cerebellopontine angle)
3. Bilateral cochlea

Sub-compartment delineation followed the posterior petrous wall per Kanzaki et al. The Koos grading classification, a binary task present in 2022, was omitted in 2023 to increase granularity within the tumour segmentation task, reflecting evolving clinical requirements for surgical planning and risk stratification [2506.12006].

## 3. Winning Approach: Vandy365 Pipeline

The top-ranked 2023 solution (Vandy365) implemented a multi-stage unsupervised domain adaptation pipeline combining advanced image-to-image translation, style transfer, segmentation, and data augmentation techniques:

- **Image-to-image translation** used an extended 3D QS-Attn architecture with adversarial loss and patch-wise contrastive InfoNCE loss:
  $$
  L_{\mathrm{adv}} = \mathbb{E}_{x\sim S}[\log D(G(x))] + \mathbb{E}_{y\sim T}[\log(1 - D(y))]
  $$
- **Site-specific style transfer** leveraged dynamic instance normalization codes to synthesize heterogeneous T2 MRI appearance.
- **Segmentation** was performed using 3D full-resolution nnU-Net backbones (combining U-Net and ResU-Net variants), followed by self-training with iterative pseudo-labeling on unlabelled real T2 scans.
- **Data augmentation** incorporated random style interpolation, structure-wise intensity scaling ($\pm 50\%$), and oversampling of challenging cases.
- **Model ensembling** fused outputs of 11 model instances, optimizing for robustness.
- **Loss functions** blended standard Dice:
  $$
  L_{\mathrm{Dice}} = 1 - \frac{2\sum_i p_{k,i}g_{k,i}}{\sum_i p_{k,i} + \sum_i g_{k,i}}
  $$
  with cross-entropy and adversarial-consistency objectives in the translation phase.

Post-processing retained only the largest connected component for each class, mitigating spurious predictions [2506.12006].

## 4. Quantitative Results and Comparative Analysis

Performance was evaluated using Dice Similarity Coefficient (DSC) and Average Symmetric Surface Distance (ASSD), aggregated across 341 test cases:

| Region           | DSC Median [IQR]    | ASSD Median [IQR] (mm) |
|------------------|--------------------|------------------------|
| VS (all)         | 87.1% [83.3–90.2]  | 0.40 [0.32–0.51]       |
| Intra-meatal VS  | 74.9% [68.8–80.0]  | 0.43 [0.32–0.57]       |
| Extra-meatal VS  | 87.3% [82.7–90.4]  | 0.40 [0.32–0.52]       |
| Cochlea          | 84.1% [81.7–86.0]  | 0.21 [0.13–0.25]       |

Outlier analysis (Figures 2–4, [2506.12006]) revealed a significant reduction in catastrophic segmentation failures for VS, particularly extra-meatal, with persistent challenges for the cochlea attributed to anatomical smallness and faint image contrast.

Retrospective winner comparison indicated:
- On the homogeneous London SC-GK dataset, 2023 VS DSC (88.6%) marginally surpassed the 2022 winner (88.5%), while cochlea DSC decreased slightly (86.3% vs. 87.1%).
- Data heterogeneity played a central role: broader protocol diversity increased segmentation robustness for tumors but imposed penalties for the cochlea, demonstrating a trade-off between generalizability and small-structure sensitivity (Figure 5).

## 5. Representative Methodologies: “Out-of-the-Box” Translation and Segmentation

Notable submissions include an approach employing the CUT model for unpaired image-to-image translation and nnU-Net for segmentation [2110.01607]:

- **CUT Model Architecture**: 2D ResNet encoder-decoder (9 residual blocks), PatchGAN discriminator (5-layer, 70×70 patches).
- **Losses**: Standard PatchGAN adversarial loss and patchwise InfoNCE contrastive loss across multiple encoder layers.
  $$
  L_{\mathrm{NCE}}^l = \mathbb{E}_{x}\Bigg[ -\sum_{i=1}^{N_l} \log\Bigg( \frac{\exp(q_i \cdot k_i / \tau)}{\sum_{j=1}^{N_l} \exp(q_i \cdot k_j / \tau)} \Bigg) \Bigg]
  $$
- **Data Augmentation**: “Augmented Tumor”—artificial reduction of tumor intensity by 50% in synthetic T2 slices to simulate real-world signal heterogeneity. Resulting dataset sizes increased from 105 to 210, improving resilience to intensity variation.
- **Segmentation**: 3D full-resolution nnU-Net configured with default parameters (U-Net, 5 depth levels, instance normalization, deep supervision).
- **Ensembling**: Averaging softmax outputs across five cross-validated models.
- **Results**: Test set mean Dice 0.8253 (Tumor: 0.8288; Cochlea: 0.8217), third place in the leaderboard. FID score for generated images: 11.15 (CUT), outperforming FastCUT (32.85). The method demonstrated stable, plug-and-play segmentation with minimal custom architecture tuning [2110.01607].

## 6. Performance Trends and Analysis

Expanded data heterogeneity led to a decrease in outlier frequency and improved performance for VS segmentation, even on homogeneous test sets. This suggests that style variation and routine-surveillance representations counteract overfitting to high-resolution planning protocols. Conversely, segmentation of the cochlea, a small organ at risk, suffered under increased data diversity due to boundary ambiguity and limited network capacity in the three-class setting. The plateauing of VS Dice scores across sequential challenge editions indicates that dataset expansion is approaching a limiting regime for current algorithmic strategies; a plausible implication is that future benchmarks require even more severe cross-modal shifts [2506.12006].

## 7. Future Directions and Recommendations

Authors recommend further increasing clinical challenge complexity, for example, by extending cross-modal benchmarks to include ultrasound or leveraging the ReMIND dataset for MRI–ultrasound adaptation. Small-structure segmentation (e.g., cochlea) may benefit from local refinement, advanced priors, or more expressive architectures. Multi-metric, rank-then-aggregate evaluations (DSC + ASSD) will remain essential for robust, clinically meaningful leaderboard stability. Reintroduction of classification or prediction tasks (such as VS growth) is encouraged once participation is sufficient [2506.12006]. 

Challenges remain for clinical deployment, especially regarding small organ segmentation under high protocol variability. Continuous evolution of the CrossMoDA series reflects a research-driven push towards benchmark tasks that better represent the realities of modern neuroimaging and surgical planning.

Source: https://www.emergentmind.com/topics/crossmoda-2023