Large-Scale Unbiased Neuroimage Indexing via 3D GPU-SIFT Filtering and Keypoint Masking.

We propose a feature extraction method via a novel description and a scalable GPU implementation (the first to our knowledge) of the 3D scale-invariant feature transform (SIFT). The feature extraction is first represented as a shallow convolutional neural network with pre-computed filters, followed by a masked keypoint analysis. We use the implementation in order to investigate feature extraction for specific instance identification on natural non-skull-stripped magnetic resonance image (MRI) neuroimaging data. The proposed implementation is invariant to 3D similarity transforms and aims to improve robustness by reducing noise and bias for image processing convolution operations. We show interpretable feature visualizations, which help explain the obtained results. We demonstrate state-of-the-art results in large-scale neuroimage family indexing experiments on 3D data from the Human Connectome Project repository, and show significant speed gains compared to a CPU implementation. The results imply that using feature extraction using SIFT for neuroimaging analysis can lead to less noisy results without the need for hard masking during preprocessing. The resulting interpretable features can help understand brain similarities between family members, and can also be used on arbitrary image modalities and anatomical structures.