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.
翻译:目前,这些模型是经过一个预处理阶段之后的图像培训,该预处理阶段涉及注册、内插、脑提取(BE,又称头骨剥离)和专家人工校正。然而,就临床实践而言,这一最后一步既乏味又耗时,因此不总是可行的,导致头骨剥离断裂,从而对肿瘤分离质量产生消极影响。然而,这一影响的程度从未为许多不同的BE方法中的任何一种方法进行过测量。在这项工作中,我们建议自动脑肿瘤分割管道,并用多种BE方法评估其性能。我们的实验表明,选择BE方法可能损害肿瘤分离性能的15.7%。此外,我们提议对非骨架浸透图像进行肿瘤分离模型培训和测试,从而有效地抛弃了输油管线上的BE级。我们的结果显示,这一方法在一定的时间内导致竞争性的性能。我们的结论是,与目前的范式不同,在高性能选择中,对高性能的肿瘤分离模型是高性能选择。