Schreier Jan, Attanasi Francesca, Laaksonen Hannu
Varian Medical Systems (United States), Palo Alto, CA, United States.
Front Oncol. 2020 May 14;10:675. doi: 10.3389/fonc.2020.00675. eCollection 2020.
As artificial intelligence for image segmentation becomes increasingly available, the question whether these solutions generalize between different hospitals and geographies arises. The present study addresses this question by comparing multi-institutional models to site-specific models. Using CT data sets from four clinics for organs-at-risk of the female breast, female pelvis and male pelvis, we differentiate between the effect from population differences and differences in clinical practice. Our study, thus, provides guidelines to hospitals, in which case the training of a custom, hospital-specific deep neural network is to be advised and when a network provided by a third-party can be used. The results show that for the organs of the female pelvis and the heart the segmentation quality is influenced solely on bases of the training set size, while the patient population variability affects the female breast segmentation quality above the effect of the training set size. In the comparison of site-specific contours on the male pelvis, we see that for a sufficiently large data set size, a custom, hospital-specific model outperforms a multi-institutional one on some of the organs. However, for small hospital-specific data sets a multi-institutional model provides the better segmentation quality.
随着用于图像分割的人工智能越来越普及，这些解决方案能否在不同医院和地区之间通用的问题随之而来。本研究通过将多机构模型与特定地点模型进行比较来解决这个问题。使用来自四家诊所的CT数据集，这些数据集涉及女性乳房、女性骨盆和男性骨盆的危险器官，我们区分了人群差异和临床实践差异的影响。因此，我们的研究为医院提供了指导方针，即在哪种情况下建议训练定制的、特定于医院的深度神经网络，以及何时可以使用第三方提供的网络。结果表明，对于女性骨盆和心脏器官，分割质量仅受训练集大小的影响，而患者人群的变异性对女性乳房分割质量的影响超过了训练集大小的影响。在男性骨盆特定地点轮廓的比较中，我们发现对于足够大的数据集规模，定制的、特定于医院的模型在某些器官上优于多机构模型。然而，对于规模较小的特定于医院的数据集，多机构模型提供了更好的分割质量。
