# K2S Challenge: From Undersampled K-Space to Automatic Segmentation
## 4 Discussion
Additionally, since all submissions were trained and tested on a fixed undersampling pattern, the robustness of solutions to other R = 8 undersampling patterns was not assessed and is an important research objective for the reconstruction community to pursue. Lastly, there is no perfect solution to the gradient inhomogeneity correction step (GRADWARP) in the standard processing pipeline of raw scanner data. Once applied, correspondence between k-space and image space is lost, meaning ordinary DICOM image segmentations would not match k-space. In the K2S dataset, segmentations were provided on images prior to GRADWARP application, meaning that gradient coil inhomogeneities manifested themselves into segmentations. Due to the difficulty in backpropagating through GRADWARP, this was viewed as the easier choice for pipeline development, with the understanding that resulting segmentations could be processed by GRADWARP to perform necessary corrections. Nonetheless, this is an unavoidable limitation that must be discussed at greater length for this and other datasets that may be released pairing k-space and tissue segmentations. In conclusion, the K2S challenge curated a landmark dataset, tasking participants with segmenting bone and cartilage from 8\(\times\) undersampled knee MRI images. Through it, the top four teams produced submissions that yielded high-quality segmentations, showing highly varied methodologies and very strong performance that was suitable for downstream biomarker analysis in cartilage thickness and bone shape assessments. Through the submissions of two teams with unconventional approaches--K-nirsh and FastMRI-AI--we clearly see that features required for radiologist annotation differ from those required for DL model input, there is room for image analysis pipeline optimization when trained end-to-end with reconstruction, and strong reconstruction is not a prerequisite for strong segmentation. These findings can motivate similar efforts for end-to-end optimization of image analysis and reconstruction tasks, not only for segmentation, but for anomaly detection, prognosis prediction, bone shape assessment, and others. Challenge organizers: A.A.T., U.B., K.T.G., R.B., F.G.G., J.L., S.M. and V.P. ; K-nirsh submission team: [PERSON], [PERSON], M.H. and C.F.B. ; UglyBarnacle submission: [PERSON] and D.D. ; FastMRI-AI submission: [PERSON] and S.J.F. ; NYU-Knee AI submission: [PERSON], R.T., H.L.d.M., K.L., M.V.W.Z. and R.R. ; Conceptualization: V.P. and S.M. ; methodology: A.A.T., U.B., K.T.G., F.G.G., J.L., S.M. and V.P. ; software: A.A.T., K.T.G. and R.B. ; validation: A.A.T., K.T.G. and R.B. ; formal analysis: A.A.T., K.T.G. and R.B. ; investigation: [PERSON], P.F., M.H., A.R., Q.v.L., S.J.F., X.Z., R.T., H.L.d.M. and K.L. ; resources: C.F.B., D.D., M.V.W.Z., R.R., S.M. and V.P. ; data curation: A.A.T., K.T.G., R.B., F.G.G., J.L. and P.G. ; writing--original draft preparation: A.A.T. ; writing--review and editing: all authors; visualization: A.A.T., K.T.G., R.B., F.G.G. and J.L. ; supervision: V.P. ; project administration: V.P. ; funding acquisition: V.P. All authors have read and agreed to the published version of the manuscript. This research was funded by a grant from the National Institutes of Health and the National Institute of Arthritis and Musculoskeletal and Skin Diseases, NIH R01 AR078762. The study was conducted in accordance with all pertinent guidelines and approved by the Institutional Review Board (Human Research Protection Program) of UCSF (IRB #17-22581, approved 26 October 2020). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. During the challenge, prospective participants were able to request access to the K2S dataset at this link. Dataset access is available from the coauthors of the challenge upon reasonable request. We would like to acknowledge [PERSON] for developing the multiclass knee cartilage segmentation pipeline.