# K2S Challenge: From Undersampled K-Space to Automatic Segmentation
## 4 Discussion
Despite this, K-nirsh yielded the strongest segmentation performance; visual inspection of K-nirsh reconstructions reveals sharp images that enhance contrast at boundaries between different tissues such as cartilage/bone boundaries, yielding an image that is perhaps easier to segment than ground truth. This demonstrates that ideal features for radiologist interpretation of an MR image can differ from those optimal for processing by an image analysis algorithm. That FastMRI-AI showed competitive segmentation performance despite directly segmenting undersampled images is a testament to this. Furthering this, was there essentially no correlation between reconstruction and segmentation metrics for any of the top submissions on a per-patient basis. There is therefore room for optimizing image analysis algorithms when trained end-to-end with reconstruction algorithms instead of training separate algorithms and inferring serially. It is important to note that segmentation performance from undersampled k-space depends not only on the segmentation algorithm but also on the undersampling pattern, which was fixed in this challenge. More complicated joint optimization of segmentation and undersampling can further improve end-to-end MRI reconstruction and image analysis outcomes [62; 63]. Apart from the specific challenge, the curation and release of the K2S dataset marks an important initiative that can seed advances in both reconstruction and image analysis algorithm development. To our knowledge, this is the largest released dataset that pairs raw k-space data with tissue segmentations (n = 300 patients, each with an 18-coil, 200-slice k-space). While a dataset of this size is more than sufficient for training most reconstruction algorithms, image annotation algorithms generally require considerably larger datasets to sufficiently represent rare anomaly classes. Our hope is that the release of this dataset can allow research groups to investigate objectives such as ROI-specific image reconstruction, end-to-end reconstruction and segmentation, and more generally end-to-end reconstruction and image analysis tasks. This challenge had some limitations. First off, the k-space provided to challenge participants had undergone R = 4 ARC, and thus does not reflect the full-length acquisition k-space that would ordinarily be undersampled. Given that the full 3D fat-suppressed CUBE sequence without ARC would require nearly 20 min for acquisition, this compromise was made to make curate a larger dataset suitable for algorithm development. Additionally, while substantial work was done by challenge organizers and radiologists (J.L. and F.G.) in inspecting segmentation quality, bone and cartilage segmentations ultimately were model generated, and were not the gold-standard manual annotations that are desired for training models. It is therefore more accurate to describe the challenge objective as achieving on 8\(\times\) undersampled data the same segmentation performance seen on fully sampled data, albeit the latter was carefully monitored and quality checked by radiologists. This tradeoff was taken to obtain a substantially larger dataset than would have been possible if exclusively using manual segmentations. We would expect these findings to hold on a dataset with purely manual segmentations but confirming so would require inferring trained models on such a dataset. Furthermore, this challenge provided a fixed undersampling pattern: a center-weighted Poisson pattern with a fully-sampled center. This undersampling pattern was selected such that potential challenge solutions would not be biased towards or against a given reconstruction backbone (i.e., compressed sensing, deep learning), but there conceivably would be room for further optimization of segmentations with respect to the undersampling pattern.