学生打败了老师：用于大脑侧脑室分割的深度神经网络MR
Student Beats the Teacher: Deep Neural Networks for Lateral Ventricles Segmentation in Brain MR
摘要：
已知心室容积及其进展与痴呆和精神分裂症等几种脑部疾病有关。因此，准确测量心室容积对于这些疾病的纵向研究至关重要，这使得自动心室分割算法是可取的。在过去的几年中，深度神经网络已经显示出在许多成像领域优于经典模型。然而，深度网络的成功取决于手动标记的数据集，这些数据集的获取成本很高，特别是对于医疗领域的高维数据。在这项工作中，我们证明了深度神经网络可以在更便宜的伪标签上进行训练（例如，由其他自动化的不太准确的方法生成），并且与标签的质量相比仍然产生更准确的分割。为了证明这一点，我们使用由传统区域生长算法生成的噪声分割标签来训练用于侧脑室分割的深度网络。然后在一个大型的手动注释测试集上，我们证明了该网络的性能显着优于用于生成网络训练标签的传统区域生长算法。我们的实验报告了一个骰子相似系数（DSC）的$0.874$的训练网络相比，$0.754$的传统区域生长算法（$p < 0.001$）。
英文摘要：
Ventricular volume and its progression are known to be linked to several brain diseases such as dementia and schizophrenia. Therefore accurate measurement of ventricle volume is vital for longitudinal studies on these disorders, making automated ventricle segmentation algorithms desirable. In the past few years, deep neural networks have shown to outperform the classical models in many imaging domains. However, the success of deep networks is dependent on manually labeled data sets, which are expensive to acquire especially for higher dimensional data in the medical domain. In this work, we show that deep neural networks can be trained on much-cheaper-to-acquire pseudo-labels (e.g., generated by other automated less accurate methods) and still produce more accurate segmentations compared to the quality of the labels. To show this, we use noisy segmentation labels generated by a conventional region growing algorithm to train a deep network for lateral ventricle segmentation. Then on a large manually annotated test set, we show that the network significantly outperforms the conventional region growing algorithm which was used to produce the training labels for the network. Our experiments report a Dice Similarity Coefficient (DSC) of $0.874$ for the trained network compared to $0.754$ for the conventional region growing algorithm ($p < 0.001$).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
