Automatic localization of normal active organs in 3D PET scans.

PET imaging captures the metabolic activity of tissues and is commonly visually interpreted by clinicians for detecting cancer, assessing tumor progression, and evaluating response to treatment. To automate accomplishing these tasks, it is important to distinguish between normal active organs and activity due to abnormal tumor growth. In this paper, we propose a deep learning method to localize and detect normal active organs visible in a 3D PET scan field-of-view. Our method adapts the deep network architecture of YOLO to detect multiple organs in 2D slices and aggregates the results to produce semantically labeled 3D bounding boxes. We evaluate our method on 479 18F-FDG PET scans of 156 patients achieving an average organ detection precision of 75-98%, recall of 94-100%, average bounding box centroid localization error of less than 14 mm, wall localization error of less than 24 mm and a mean IOU of up to 72%.

Directed Functional Networks in Alzheimer's Disease: Disruption of Global and Local Connectivity Measures.

Techniques available in graph theory can be applied to signals recorded from human brain. In network analysis of EEG signals, the individual nodes are EEG sensor locations and the edges correspond to functional relations between them that are extracted from EEG time series. In this paper, we study EEG-based directed functional networks in Alzheimer's disease (AD). To this end, directed connectivity matrices of 25 AD patients and 26 healthy subjects are processed and a number of meaningful graph theory metrics are studied. Our data show that functional networks of AD brains have significantly reduced global connectivity in alpha and beta bands (P < 0.05). The AD brains have significantly higher local connectivity than healthy controls in alpha and beta bands. This decreased profile in global connectivity can be linked to compensatory increased local connectivity as a result of wide-spread decline in the long-range connections. We also study resiliency of brain networks against targeted attack to hub nodes and find that AD networks are less resilient than healthy brains in alpha and beta bands.