  Nanoscale secondary ion mass spectrometry (nanoSIMS) and fluorescence in situ
hybridization (FISH) microscopy provide high-resolution, multimodal image
representations of the identity and cell activity respectively of targeted
microbial communities in microbiological research. Despite its importance to
microbiologists, multimodal registration of FISH and nanoSIMS images is
challenging given the morphological distortion and background noise in both
images. In this study, we use convolutional neural networks (CNNs) for
multiscale feature extraction, shape context for computation of the minimum
transformation cost feature matching and the thin-plate spline (TPS) model for
multimodal registration of the FISH and nanoSIMS images. Registration accuracy
was quantitatively assessed against manually registered images, at both, the
pixel and structural levels using standard metrics. Although all six tested CNN
models performed well, ResNet18 was observed to outperform VGG16, VGG19,
GoogLeNet and ShuffleNet and ResNet101 based on most metrics. This study
demonstrates the utility of CNNs in the registration of multimodal images with
significant background noise and morphology distortion. We also show aggregate
shape, preserved by binarization, to be a robust feature for registering
multimodal microbiology-related images.
