Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment

Wenyi Lin; Kyle Hasenstab; Guilherme Moura Cunha; Armin Schwartzman

doi:10.1038/s41598-020-77264-y

Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment

Sci Rep. 2020 Nov 23;10(1):20336. doi: 10.1038/s41598-020-77264-y.

Authors

Wenyi Lin¹, Kyle Hasenstab², Guilherme Moura Cunha³, Armin Schwartzman^{4

5}

Affiliations

¹ Division of Biostatistics, Department of Family Medicine and Public Health, University of California San Diego, La Jolla, 92093, USA. wel316@ucsd.edu.
² Department of Mathematics and Statistics, San Diego State University, San Diego, CA, 92182, USA.
³ Liver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, 92093, USA.
⁴ Division of Biostatistics, Department of Family Medicine and Public Health, University of California San Diego, La Jolla, 92093, USA.
⁵ Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, 92093, USA.

Abstract

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.