  Patient motion during the magnetic resonance imaging (MRI) acquisition
process results in motion artifacts, which limits the ability of radiologists
to provide a quantitative assessment of a condition visualized. Often times,
radiologists either "see through" the artifacts with reduced diagnostic
confidence, or the MR scans are rejected and patients are asked to be recalled
and re-scanned. Presently, there are many published approaches that focus on
MRI artifact detection and correction. However, the key question of the bias
exhibited by these algorithms on motion corrupted MRI images is still
unanswered. In this paper, we seek to quantify the bias in terms of the impact
that different levels of motion artifacts have on the performance of neural
networks engaged in a lesion segmentation task. Additionally, we explore the
effect of a different learning strategy, curriculum learning, on the
segmentation performance. Our results suggest that a network trained using
curriculum learning is effective at compensating for different levels of motion
artifacts, and improved the segmentation performance by ~9%-15% (p < 0.05) when
compared against a conventional shuffled learning strategy on the same motion
data. Within each motion category, it either improved or maintained the dice
score. To the best of our knowledge, we are the first to quantitatively assess
the segmentation bias on various levels of motion artifacts present in a brain
MRI image.
