ArticleBioengineering (Basel, Switzerland)2023
K2S Challenge: From Undersampled K-Space to Automatic Segmentation.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of artificial intelligence competitions in radiology: a focus on design, evaluation, and trends.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Pooled it
- Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes.NPJ digital medicine · 2026Article
- Artificial Intelligence-Based Proximal Bone Shape Asymmetry Analysis and Clinical Correlation with Cartilage Relaxation Times and Functional Activity.Bioengineering (Basel, Switzerland) · 2026Article
- A memory based model for cartilage and meniscus segmentation in 3D knee MRI.Scientific reports · 2025Article
- Artificial intelligence in musculoskeletal applications: a primer for radiologists.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Improving radiologist detection of meniscal abnormality on undersampled, deep learning reconstructed knee MRI.Radiology advances · 2025Article
- Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration.ArXiv · 2025Article
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Synthetic Knee MRI TBioengineering (Basel, Switzerland) · 2023Article
- AI for PET image reconstruction.The British journal of radiology · 2023Review
- Synthetic Inflammation Imaging with PatchGAN Deep Learning Networks.Bioengineering (Basel, Switzerland) · 2023Article
- AI in MRI: Computational Frameworks for a Faster, Optimized, and Automated Imaging Workflow.Bioengineering (Basel, Switzerland) · 2023Article
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23 authors.
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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.
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