# K2S Challenge: From Undersampled K-Space to Automatic Segmentation
## 1 Introduction
In other words, reconstruction algorithm image outputs are optimized for visually appealing images and radiologist interpretation, but what if their outputs were instead intended as features for subsequent image analysis pipeline input? Could the features required for accurate radiologist readings with respect to ground truth differ from those required for DL image analysis pipeline input to yield strong performance? More generally, if image reconstruction and annotation are viewed as end-to-end rather than serial tasks, is it possible to attain stronger image analysis performance? To answer these questions, we hosted the K2S challenge at the 25 th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in Singapore. Previously, challenges and/or the releases of large datasets have spurred major advances in the MR research community. The release of the Osteoarthritis Initiative (OAI) and Multicenter Osteoarthritis Study (MOST) precipitated substantial advances in understanding osteoarthritis, total knee replacement, and knee pain, among others [35; 36; 37; 38; 39]. On the other hand, the fastMRI challenge was crucial in (1) making image reconstruction more accessible to the MR research community by releasing large datasets including raw k-space, and (2) seeding major advances in reconstruction research such as popularizingunrolled DL architectures [40; 41; 42]. Our objective was to fill a similar niche in the end-to-end reconstruction and image analysis space. As such, we curated the K2S dataset, which consists of 300 patients that underwent 3D fat-suppressed knee scans, for each of whom k-space data and radiologist-approved 6-compartment tissue segmentations were released. The use of Fourier-transformed DICOM images as k-space would be problematic, not maintaining consistency with the multicoil nature of most MR acquisitions and the numerous post-processing steps to convert raw multicoil k-space into DICOM images, while also likely overstating performance [43]; importantly, our released dataset thus was of raw multicoil k-space data. Challenge participants were to train algorithms that segmented knee bones and cartilage from 8\(\times\) undersampled acquisitions. Winners were selected using a weighted dice similarity coefficient (DSC) that assessed the accuracy of resulting segmentations, but additional analyses were conducted to assess segmentation quality, determine if strong image reconstruction was a prerequisite for strong segmentation, and age the suitability of submitted segmentations for biomarker analysis [44]. In short, the contributions of the K2S challenge and this paper are as follows:  
* Reframing image reconstruction and annotation as end-to-end tasks for an eventual clinical workflow rather than sequential steps. * Curating a large dataset (n = 300) with 3D raw k-space data and tissue segmentations to allow training of segmentation algorithms directly from undersampled k-space, and whatever additional research objectives may emerge from having raw k-space and segmentations in the same dataset. * Investigating whether strong image reconstructions are a prerequisite for strong tissue segmentations. * Assessing if segmentation algorithms trained from 8\(\times\) undersampled data are suitable for biomarker analysis.