Table 3.

Method summary of all participating teams, ordered alphabatically by team name. For detailed descriptions and relevant references cited, please refer to the Appendix A. Awards lists the number of first (gold), second (silver) and third (bronze) prizes a team has won in the TopCoW challenge. The winning team names are in bold.

Team	Track	Task	Awards	Method	Highlights
2i_mtl	CTA	Binary		3D AttentionUNet for segmentation; 3D autoencoder to mitigate false positives	- Use provided CoW ROI for training - Image & mask input to autoencoder
agaldran	CTA MRA	Binary Multiclass		3D dynamic UNet	- Ensembling - Cross-validation on patch size
EURECOM	CTA MRA	Binary		SynthSeg for Brain mask extraction; A2V for multi-modal segmentation	- Single model for both modalities - 2D axial slice input - Use provided CoW ROI for patch extraction
gbCoW	MRA	Binary Multiclass		3D nnUNet	- Only multiclass labels used - Turned off data augmentation
gl	CTA MRA	Multiclass		Atlas registration for custom ROI extraction; 3D MedNexT & UX-Net for binary and subsequent multiclass segmentation	- Dataset specific atlas - Binary mask input for multiclass segmentation - Ensembling
IWantToGoToCanada	CTA	Binary Multiclass		3D nnUNet for binary segmentation; 3D Swin-UNETR for subsequent multiclass segmentation	- Binary mask input for multiclass segmentation
junqiangchen	CTA MRA	Binary Multiclass		VNet3D for custom ROI extraction; VNet3D for ROI segmentation	- Brain mask extraction - Binary mask for custom ROI
lWM	CTA MRA	Binary Multiclass		3D ResidualUNet	- Custom end-to-end UNet
NexToU	CTA MRA	Binary Multiclass		3D nnUNet for low-res binary segmentation; NexToU architecture for full-res cascading	- Centerline boundary Dice (cb-Dice) - Binary topological interaction (BTI) module
NIC-VICOROB-1	CTA MRA	Binary Multiclass		3D nnUNet	- Ensembling - Binary mask input for CT multiclass
NIC-VICOROB-2	CTA MRA	Binary Multiclass		3D AttentionUNet for binary segmentation; 2D AttentionUNet for subsequent multiclass segmentation	- Axial slice input for multiclass segmentation - Binary mask input for multiclass segmentation - Use provided CoW ROI for 3D patch extraction
Organizers	CTA MRA	Multiclass		nnDetection for custom ROI extraction; 3D nnUNet for multiclass segmentation	- Image registration for data augmentation - ROI object detection - Ensembling
refrain	MRA	Binary Multiclass		Atlas registration for custom ROI extraction; 3D nnUNet for segmentation	- Data augmentation for rare CoW variants - Segment specific loss weighting
sjtu_eiee_2-426lab	CTA	Binary Multiclass		3D nnUNet for custom ROI extraction; 3D nnUNet for ROI segmentation	- Binary mask for custom ROI
UB-VTL	CTA MRA	Binary		Modified 3D Brave-Net	- clDice loss for connectedness - Residual connections & PReLu activations - Use provided CoW ROI for patch extraction
UW	MRA	Binary		3D nnUNet	- 3-component loss (Dice + CE + TopK) - Ensembling
WilliWillsWissen	CTA MRA	Binary Multiclass		3D nnUNet	- clDice/skeleton recall for connectedness - Training on both modalities - Ensembling
ysato	MRA	Binary		Auto vessel thresholding; Region growing	- Non-deep learning algorithm - Short inference time (~15 s per case) - Little computing power needed (done on CPU)