We propose a random forest classifier for identifying adequacy of liver MR images using handcrafted (HC) features and deep convolutional neural networks (CNNs), and analyze the relative role of these two components in relation to the training sample size. The HC features, specifically developed for this application, include Gaussian mixture models, Euler characteristic curves and texture analysis. Using HC features outperforms the CNN for smaller sample sizes and with increased interpretability. On the other hand, with enough training data, the combined classifier outperforms the models trained with HC features or CNN features alone. These results illustrate the added value of HC features with respect to CNNs, especially when insufficient data is available, as is often found in clinical studies.

我们提出了一个随机森林分类器，用于使用手工（HC）特征和深度卷积神经网络（CNN）识别肝脏MR图像的适当性，并分析这两个成分相对于训练样本量的相对作用。专为此应用开发的HC功能包括高斯混合模型，欧拉特征曲线和纹理分析。使用HC功能时，对于较小的样本量和增强的可解释性，其性能优于CNN。另一方面，在具有足够的训练数据的情况下，组合分类器的性能优于仅使用HC特征或CNN特征训练的模型。这些结果说明了与CNN相关的HC功能的附加值，尤其是在没有足够数据的情况下，如在临床研究中经常发现的那样。