论文标题
评估神经网络的病变细分偏置运动损坏的大脑MRI
Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRI
论文作者
论文摘要
磁共振成像（MRI）采集过程中的患者运动会导致运动伪像，这限制了放射学家提供对可视化状况的定量评估的能力。通常，放射科医生要么“查看”以诊断信心降低的工件，要么拒绝MR扫描，并要求患者被召回和重新扫描。目前，有许多已发表的方法着眼于MRI伪影检测和校正。但是，这些算法对运动损坏的MRI图像所表现出的偏见的关键问题仍未得到解答。在本文中，我们试图根据不同运动伪像对从事病变细分任务的神经网络的性能的影响来量化偏见。此外，我们探讨了不同学习策略，课程学习对细分表现的影响。我们的结果表明，使用课程学习训练的网络有效地补偿了不同水平的运动伪像，并将分割性能提高了〜9％-15％（P <0.05）（p <0.05），与在相同运动数据上进行的常规洗牌学习策略进行了比较。在每个运动类别中，它都提高或维持骰子得分。据我们所知，我们是第一个定量评估脑MRI图像中存在的各种运动伪像的分割偏差的人。
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.
