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Abstract #0859

K2S Challenge: From Undersampled K-Space to Automatic Segmentation

Aniket Tolpadi1, Upasana Bharadwaj1, Kenneth Gao1, Rupsa Bhattacharjee1, Felix Gassert1, Johanna Luitjens1, Jan Nikolas Morshuis2,3, Paul Fischer2, Matthias Hein2, Christian F. Baumgartner2, Artem Razumov4, Dmitry Dylov4, Quintin van Lohuizen5, Stefan Fransen5, Xiaoxia Zhang6, Radhika Tibrewaka6, Hector Lise de Moura6, Kangning Liu6, Marcelo Zibetti6, Ravinder Regatte6, Sharmila Majumdar1, and Valentina Pedoia1
1Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States, 2Cluster of Excellence Machine Learning, University of Tübingen, Tübingen, Germany, 3International Max Planck Research School for Intelligent Systems, Tübingen, Germany, 4Skolkovo Institute of Science and Technology, Moscow, Russian Federation, 5Department of Radiology, University Medical Center Groningen, Groningen, Netherlands, 6Center for Advanced Imaging Innovation and Research, New York University Grossman School of Medicine, New York, NY, United States

Synopsis

Keywords: Image Reconstruction, MSKImage reconstruction and downstream tasks have typically been treated independently by the image processing community, but we hypothesized performing them end-to-end could facilitate further optimization. To these ends, UCSF organized the K2S challenge, where challenge participants were tasked with segmenting bone and cartilage from 8X undersampled knee MRI acquisitions. Top challenge submissions produced high-quality segmentations maintaining fidelity to ground truth, but strong reconstruction performance proved not to be required for accurate tissue segmentation, and there was no correlation between reconstruction and segmentation performance. This challenge showed reconstruction algorithms can be optimized for downstream tasks in an end-to-end fashion.

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Keywords