Two-Dimensional and Three-Dimensional Time-of-Flight
Secondary Ion Mass Spectrometry Image Feature Extraction Using a Spatially
Aware Convolutional Autoencoder
posted on 2022-05-26, 13:34authored byWil Gardner, David A. Winkler, Suzanne M. Cutts, Steven A. Torney, Geoffrey A. Pietersz, Benjamin W. Muir, Paul J. Pigram
Feature
extraction algorithms are an important class of unsupervised
methods used to reduce data dimensionality. They have been applied
extensively for time-of-flight secondary ion mass spectrometry (ToF-SIMS)
imagingcommonly, matrix factorization (MF) techniques such
as principal component analysis have been used. A limitation of MF
is the assumption of linearity, which is generally not accurate for
ToF-SIMS data. Recently, nonlinear autoencoders have been shown to
outperform MF techniques for ToF-SIMS image feature extraction. However,
another limitation of most feature extraction methods (including autoencoders)
that is particularly important for hyperspectral data is that they
do not consider spatial information. To address this limitation, we
describe the application of the convolutional autoencoder (CNNAE)
to hyperspectral ToF-SIMS imaging data. The CNNAE is an artificial
neural network developed specifically for hyperspectral data that
uses convolutional layers for image encoding, thereby explicitly incorporating
pixel neighborhood information. We compared the performance of the
CNNAE with other common feature extraction algorithms for two biological
ToF-SIMS imaging data sets. We investigated the extracted features
and used the dimensionality-reduced data to train additional ML algorithms.
By converting two-dimensional convolutional layers to three-dimensional
(3D), we also showed how the CNNAE can be extended to 3D ToF-SIMS
images. In general, the CNNAE produced features with significantly
higher contrast and autocorrelation than other techniques. Furthermore,
histologically recognizable features in the data were more accurately
represented. The extension of the CNNAE to 3D data also provided an
important proof of principle for the analysis of more complex 3D data
sets.