# K2S Challenge: From Undersampled K-Space to Automatic Segmentation
## 3 Results
### Biomarker Analysis: Cartilage Thickness  
Example femoral cartilage thickness maps projected onto the femur are shown in Figure 9, with corresponding femoral cartilage segmentation DSCs. These results elucidate added complexity: while FastMRI-AI and NYU-Knee AI lagged K-nirsh and UglyBarnacle in weighted DSC, they did a better job preserving certain thick and thin cartilage regions. Qualitatively, however, these maps show K-nirsh, UglyBarnacle, and FastMRI-AI perform especially well in reconstructing cartilage thicknesses; Bland-Altman plots in Figure 10 confirm these results, showing cartilage thicknesses across all three compartments were predicted with minimal bias and strong fidelity to ground truth by these three teams. Interestingly, bias in retaining femoral cartilage thicknesses decreased with larger ground truth cartilage thicknesses, regardless of submission. More granularly, while fastMRI-AI slightly overestimated patellar cartilage thicknesses, they also reflected the least bias in maintaining femoral cartilage thickness, showing some discordance between weighted DSC and downstream biomarker analysis. Contrarily, thicknesses were overestimated by NYU-Knee AI, particularly in tibiofemoral regions. In comparing the top two challenge finishers, K-nirsh and UglyBarnacle, biases in predicted cartilage thicknesses were slightly lower for UglyBarnacle in femoral and tibial cartilage, and slightly higher in patellar cartilage  
Figure 7: Sagittal slice segmentations overlaid on intermediate pipeline reconstructions, with reconstruction and segmentation metrics for the volume including the slice displayed. Background anatomy slices were thus blurrier for some teams than for others, as different teams had different quality intermediate pipeline reconstruction outputs. In this example, segmentation quality was strong for all top submissions, with only some overestimation of cartilage thickness from the NYU-Knee AI pipeline being apparent. K-nirsh maintains a slight edge over UglyBarnacle in reconstruction metrics for this volume. (UglyBarnacle: femoral: 0.088 \(\pm\) 0.07, tibial: 0.036 \(\pm\) 0.09, patellar: 0.114 \(\pm\) 0.13; K-nirsh: femoral: 0.096 \(\pm\) 0.08, tibial: 0.049 \(\pm\) 0.09, patellar: 0.097 \(\pm\) 0.11; all in units of mm, mean \(\pm\) 1 s.d.). However, paired t-tests showed none of these differences were significant even after Bonferroni correction (n = 50, \(\alpha\) = 0.05). Figure 8: Reconstruction metrics (nRMSE, PSNR, SSIM) plotted against weighted DSC for each of the top four submissions, with each point denoting a subject in the test set (n = 50). [PERSON]’s correlation coefficient was calculated for each pair and is displayed on the chart, indicating that at absolute best, there was a weak correlation between segmentation and reconstruction metrics, and that in most cases, there was no or even negative correlation. Figure 9: Femoral cartilage thickness maps projected onto voxel-based femoral bone shapes for each of the top 4 teams, as well as ground truth. While all submissions exhibit a degree of smoothness that is not reflected in the ground truth, the top three especially were strong in preserving cartilage thicknesses (K-nirsh, UglyBarnacle, FastMRI-AI), with NYU-Knee AI slightly overestimating cartilage thicknesses but still preserving key features in some regions. Correlation plots in Figure 10 showed K-nirsh, UglyBarnacle, and NYU-Knee AI yielded high Pearson correlation coefficients with respect to ground truth, indicating high-quality segmentations. Interestingly, UglyBarnacle showed a slightly higher correlation to ground truth cartilage thickness in tibiofemoral cartilage than K-nirsh, despite lower DSCs in both tissues. Visually, FastMRI-AI also appeared to show a strong correlation between predicted and ground truth cartilage thickness, although poor prediction in one case appeared to severely degrade the correlation coefficient.