K2S Challenge
| العنوان: | K2S Challenge |
|---|---|
| المؤلفون: | 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. Zibetti, Ravinder Regatte, Sharmila Majumdar, Valentina Pedoia |
| المصدر: | Bioengineering, 10(2):267. AIMS Press Bioengineering Volume 10 Issue 2 Pages: 267 Bioengineering (Basel, Switzerland), vol 10, iss 2 |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | musculoskeletal, segmentation, Biomedical Imaging, deep learning, magnetic resonance imaging, multi-task learning, Bioengineering, image reconstruction, ddc, Article, compressed sensing |
| الوصف: | 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. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2306-5354 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5246b507509e078e91838ddd23756a8b https://hdl.handle.net/11370/bf773d13-d455-49d2-9cf9-5bef4155ed3a |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....5246b507509e078e91838ddd23756a8b |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 23065354 |
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