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%.