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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