K2S Challenge: From Undersampled K-Space to Automatic Segmentation

Aniket A. Tolpadi*, Upasana Bharadwaj, Kenneth T. Gao, Rupsa Bhattacharjee, Felix G. Gassert, Johanna Luitjens, Paula Giesler, Jan Nikolas Morshuis, Paul Fischer, Matthias Hein, Christian F. Baumgartner, Artem Razumov, Dmitry Dylov, Quintin van Lohuizen, Stefan J. Fransen, Xiaoxia Zhang, Radhika Tibrewala, Hector Lise de Moura, Kangning Liu, Marcelo V.W. ZibettiRavinder Regatte, Sharmila Majumdar, Valentina Pedoia

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    6 Citations (Scopus)
    52 Downloads (Pure)

    Abstract

    Magnetic Resonance Imaging (MRI) offers strong soft tissue contrast but suffers from long acquisition times and requires tedious annotation from radiologists. Traditionally, these challenges have been addressed separately with reconstruction and image analysis algorithms. To see if performance could be improved by treating both as end-to-end, we hosted the K2S challenge, in which challenge participants segmented knee bones and cartilage from 8× undersampled k-space. We curated the 300-patient K2S dataset of multicoil raw k-space and radiologist quality-checked segmentations. 87 teams registered for the challenge and there were 12 submissions, varying in methodologies from serial reconstruction and segmentation to end-to-end networks to another that eschewed a reconstruction algorithm altogether. Four teams produced strong submissions, with the winner having a weighted Dice Similarity Coefficient of 0.910 ± 0.021 across knee bones and cartilage. Interestingly, there was no correlation between reconstruction and segmentation metrics. Further analysis showed the top four submissions were suitable for downstream biomarker analysis, largely preserving cartilage thicknesses and key bone shape features with respect to ground truth. K2S thus showed the value in considering reconstruction and image analysis as end-to-end tasks, as this leaves room for optimization while more realistically reflecting the long-term use case of tools being developed by the MR community.

    Original languageEnglish
    Article number267
    Number of pages24
    JournalBioengineering
    Volume10
    Issue number2
    DOIs
    Publication statusPublished - 18-Feb-2023

    Keywords

    • compressed sensing
    • deep learning
    • image reconstruction
    • magnetic resonance imaging
    • multi-task learning
    • musculoskeletal
    • segmentation

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