Characterizing blood vessels in digital images is important for the diagnosis of many types of diseases as well as for assisting current researches regarding vascular systems. The automated analysis of blood vessels typically requires the identification, or segmentation, of the blood vessels in an image or a set of images, which is usually a challenging task. Convolutional Neural Networks (CNNs) have been shown to provide excellent results regarding the segmentation of blood vessels. One important aspect of CNNs is that they can be trained on large amounts of data and then be made available, for instance, in image processing software for wide use. The pre-trained CNNs can then be easily applied in downstream blood vessel characterization tasks such as the calculation of the length, tortuosity, or caliber of the blood vessels. Yet, it is still unclear if pre-trained CNNs can provide robust, unbiased, results on downstream tasks when applied to datasets that they were not trained on. Here, we focus on measuring the tortuosity of blood vessels and investigate to which extent CNNs may provide biased tortuosity values even after fine-tuning the network to the new dataset under study. We show that the tortuosity values obtained by a CNN trained from scratch on a dataset may not agree with those obtained by a fine-tuned network that was pre-trained on a dataset having different tortuosity statistics. In addition, we show that the improvement in segmentation performance when fine-tuning the network does not necessarily lead to a respective improvement on the estimation of the tortuosity. To mitigate the aforementioned issues, we propose the application of specific data augmentation techniques even in situations where they do not improve segmentation performance.
翻译:在数字图像中标注血管对于诊断许多类型的疾病以及协助目前有关血管系统的研究非常重要。对血管的自动分析通常要求用图像或一组图像来识别或分解血管中的血管,这通常是一项艰巨的任务。进化神经网络(CNNs)已经显示在血液容器的分解方面提供了极好的结果。CNN公司的一个重要方面是,它们可以接受大量数据的培训,然后可以提供,例如,图像处理软件,供广泛使用。经过培训的CNN公司可以很容易地应用于下游血液容器的定性任务,例如,用图像或一组图像来识别或分解血管中的血管,而这通常是一项具有挑战性的任务。然而,尚不清楚的是,经过培训的CNN网络网络网络是否能够提供稳健的、公正的下游任务成果,当它们被应用到没有经过培训的数据集时,我们注重测量血液容器的异常性能,然后调查CNN公司在多大程度上可以提供有偏向的偏向性值。在经过精细调整的网络应用后,我们无法在网络中提出某种精细的分数,因此,在通过研究获得的数据可以显示某种特定的性数据时,从而显示在网络上产生不同的性能。我们如何改进。我们如何改进。我们从网络数据时,我们获得的精确的精确地显示在网络数据时,我们如何改进。我们通过研究获得的精确地显示的精确性能。我们如何使网络的数据表明,从而显示在网络的精确性能显示,我们通过新的数据来显示,从而显示获得的精确性能得到的数据。