Umeå University, Department of Diagnostics and Intervention, Umeå, Sweden.
University of Szeged, Albert Szent-Györgyi Medical School, Department of Radiology, Szeged, Hungary.
Sci Data. 2024 Oct 8;11(1):1097. doi: 10.1038/s41597-024-03945-2.
Manual segmentations are considered the gold standard for ground truth in machine learning applications. Such tasks are tedious and time-consuming, albeit necessary to train reliable models. In this work, we present a dataset with expert segmentations of the prostatic zones and urethra for 200 randomly selected patients from the PROSTATEx dataset. Notably, independent duplicate segmentations were performed for 40 patients, providing inter-reader variability data. This results in a total of 240 segmentations. This dataset can be used to train machine learning models or serve as an external test set for evaluating models trained on private data, thereby addressing a current gap in the field. The delineated structures and terminology adhere to the latest Prostate Imaging Reporting and Data Systems v2.1 guidelines, ensuring consistency.
手动分割被认为是机器学习应用中地面实况的金标准。此类任务既繁琐又耗时,但对于训练可靠的模型却是必要的。在这项工作中,我们提供了一个数据集,其中包含来自 PROSTATEx 数据集的 200 名随机患者的前列腺区域和尿道的专家分割。值得注意的是,对 40 名患者进行了独立的重复分割,提供了读者间变异性数据。这总共产生了 240 个分割。该数据集可用于训练机器学习模型,也可作为评估在私有数据上训练的模型的外部测试集,从而解决该领域的一个当前空白。所描绘的结构和术语符合最新的前列腺成像报告和数据系统 v2.1 指南,确保了一致性。