Table 1.

Recent biomedical segmentation challenges and some of their publications

Challenge	Reference	Dataset	Method	Performance (best results)
CHAOS	Conze et al. [62]	80 patients (40 CT, 40 MRI scans)	Conditional generative adversarial network with a partially pre-trained generator	Dice: 97.95 ± 0.27 ASSD: 0.76 ± 0.16  (performance of cGv16pUNet1-1)
FLARE	Zhang et al. [14]	511 CT scans and annotations for 4 abdominal organs	Context-aware efficient encoder-decoder model with anisotropic pyramid pooling	Dice: 96.5 ± 6.1 NSD: 87.8 ± 11.2 (performance of efficientSegNet)
KiTS	Chen et al. [63]	300 CT scans	nnU-Net-based coarse-to-fine segmentation framework	Dice: 90.99 NSD: 83.48