Despite being one of the most prevalent forms of cancer, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection and treatment. Computational methods can help make this detection process considerably faster and more robust. However, some modern machine-learning approaches require accurate segmentation of the prostate gland and the index lesion. Since performing manual segmentations is a very time-consuming task, and highly prone to inter-observer variability, there is a need to develop robust semi-automatic segmentation models. In this work, we leverage the large and highly diverse ProstateNet dataset, which includes 638 whole gland and 461 lesion segmentation masks, from 3 different scanner manufacturers provided by 14 institutions, in addition to other 3 independent public datasets, to train accurate and robust segmentation models for the whole prostate gland, zones and lesions. We show that models trained on large amounts of diverse data are better at generalizing to data from other institutions and obtained with other manufacturers, outperforming models trained on single-institution single-manufacturer datasets in all segmentation tasks. Furthermore, we show that lesion segmentation models trained on ProstateNet can be reliably used as lesion detection models.

中文翻译：

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使用多中心回顾性数据分析整个前列腺的域转移、区域和病变分割和检测

尽管前列腺癌 (PCa) 是最常见的癌症之一，但只要及时发现和治疗，其存活率却相当高。计算方法可以帮助使检测过程变得更快、更稳健。然而，一些现代机器学习方法需要精确分割前列腺和索引病变。由于执行手动分割是一项非常耗时的任务，并且很容易出现观察者之间的差异，因此需要开发强大的半自动分割模型。在这项工作中，我们利用大型且高度多样化的 ProstateNet 数据集，其中包括来自 14 个机构提供的 3 个不同扫描仪制造商的 638 个全腺体和 461 个病变分割掩模，以及其他 3 个独立的公共数据集，来训练准确且稳健的分割整个前列腺、区域和病变的模型。我们表明，在大量不同数据上训练的模型能够更好地泛化到来自其他机构和其他制造商获得的数据，在所有细分任务中都优于在单一机构单一制造商数据集上训练的模型。此外，我们表明在 ProstateNet 上训练的病变分割模型可以可靠地用作病变检测模型。