论文标题
朝着完全自动化的深度学习脑肿瘤分割：大脑提取是否需要？
Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?
论文作者
论文摘要
最新的脑肿瘤分割基于应用于多模式MRI的深度学习模型。当前，这些模型在预处理阶段之后对图像进行了培训，该阶段涉及注册，插值，脑提取（也称为头骨剥离）和专家手动校正。但是，对于临床实践，最后一步是乏味且耗时的，因此并非总是可行的，导致颅骨脱落断层，可能会对肿瘤分割质量产生负面影响。尽管如此，对于可用的许多不同方法中的任何一种，这种影响的程度尚未得到测量。在这项工作中，我们提出了一种自动的脑肿瘤分割管道，并通过多种方法评估其性能。我们的实验表明，选择方法的选择可以损害多达15.7％的肿瘤分割性能。此外，我们提出了在非kull划分的图像上进行训练和测试肿瘤分割模型，从而有效地从管道中丢弃了BE步骤。我们的结果表明，这种方法会导致一小部分时间的竞争性能。我们得出的结论是，与当前的范式相反，当需要在临床实践中高性能时，在非库尔分裂图像上的训练肿瘤分割模型可能是最好的选择。
State-of-the-art brain tumor segmentation is based on deep learning models applied to multi-modal MRIs. Currently, these models are trained on images after a preprocessing stage that involves registration, interpolation, brain extraction (BE, also known as skull-stripping) and manual correction by an expert. However, for clinical practice, this last step is tedious and time-consuming and, therefore, not always feasible, resulting in skull-stripping faults that can negatively impact the tumor segmentation quality. Still, the extent of this impact has never been measured for any of the many different BE methods available. In this work, we propose an automatic brain tumor segmentation pipeline and evaluate its performance with multiple BE methods. Our experiments show that the choice of a BE method can compromise up to 15.7% of the tumor segmentation performance. Moreover, we propose training and testing tumor segmentation models on non-skull-stripped images, effectively discarding the BE step from the pipeline. Our results show that this approach leads to a competitive performance at a fraction of the time. We conclude that, in contrast to the current paradigm, training tumor segmentation models on non-skull-stripped images can be the best option when high performance in clinical practice is desired.
