Creating a dataset for training supervised machine learning algorithms can be a demanding task. This is especially true for medical image segmentation since one or more specialists are usually required for image annotation, and creating ground truth labels for just a single image can take up to several hours. In addition, it is paramount that the annotated samples represent well the different conditions that might affect the imaged tissues as well as possible changes in the image acquisition process. This can only be achieved by considering samples that are typical in the dataset as well as atypical, or even outlier, samples. We introduce VessMAP, a heterogeneous blood vessel segmentation dataset acquired by carefully sampling relevant images from a larger non-annotated dataset. A methodology was developed to select both prototypical and atypical samples from the base dataset, thus defining an assorted set of images that can be used for measuring the performance of segmentation algorithms on samples that are highly distinct from each other. To demonstrate the potential of the new dataset, we show that the validation performance of a neural network changes significantly depending on the splits used for training the network.

翻译：创建用于训练监督式机器学习算法的数据集可能是一项艰巨任务。尤其对于医学图像分割而言，通常需要一名或多名专家进行图像标注，而单张图像的真实标签制作可能耗时数小时。此外，标注样本必须充分代表可能影响成像组织的不同条件以及图像采集过程中的潜在变化。这只有通过同时考虑数据集中的典型样本、非典型样本甚至离群样本才能实现。我们提出VessMAP——一个通过从更大规模未标注数据集中精心采样相关图像而构建的异质性血管分割数据集。我们开发了一种方法论，从原始数据集中选取原型样本与非典型样本，从而定义一组多样化的图像，可用于衡量分割算法在高度异质样本上的表现。为展示该数据集的潜力，我们验证了神经网络的验证性能会因训练时使用的数据划分方式而产生显著差异。