Equivariant and Denoising CNNs to Decouple Intensity and Spatial Features for Motion Tracking in Fetal Brain MRIDownload PDF

Published: 28 Apr 2023, Last Modified: 02 Jun 2023MIDL 2023 Short paper track PosterReaders: Everyone
Keywords: Equivariant CNN, Denoising, Motion tracking, Fetal brain MRI
TL;DR: We present a new registration-based motion tracking strategy that leverages equivariant and denoising CNNs to decouple intensity and spatial features.
Abstract: Equivariance in convolutional neural networks (CNN) has been a long-sought property, as it would ensure robustness to expected effects in the data. Convolutional filters are by nature translation-equivariant, and rotation-equivariant kernels were proposed recently. While these filters can be paired with learnable weights to form equivariant networks (E-CNN), we show here that such E-CNNs have a limited learning capacity, which makes them fragile against even slight changes in intensity distribution. This sensitivity to intensity changes presents a major challenge in medical imaging where many noise sources can randomly corrupt the data, even for consecutive scans of the same subject. Here, we propose a hybrid architecture that successively decouples intensity and spatial features: we first remove irrelevant noise in the data with a denoising CNN, and then use an E-CNN to extract robust spatial features. We demonstrate our method for motion tracking in fetal brain MRI, where it considerably outperforms standard CNNs and E-CNNs.
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