Deep learning (DL) has become widely used for medical image segmentation in recent years. However, despite these advances, there are still problems for which DL-based segmentation fails. Recently, some DL approaches had a breakthrough by using anatomical information which is the crucial cue for manual segmentation. In this paper, we provide a review of anatomy-aided DL for medical image segmentation which covers systematically summarized anatomical information categories and corresponding representation methods. We address known and potentially solvable challenges in anatomy-aided DL and present a categorized methodology overview on using anatomical information with DL from over 70 papers. Finally, we discuss the strengths and limitations of the current anatomy-aided DL approaches and suggest potential future work.

PET measurements of tracers with a lower dose rate or short radioactive half life suffer from extremely low SNRs. In these cases standard reconstruction methods (OSEM, EM, filtered backprojection) deliver unsatisfactory and noisy results. Here, we propose to introduce nonlinear variational methods into the reconstruction process to make an efficient use of a-priori information and to attain improved imaging results. We illustrate our technique by evaluating cardiac H2¹⁵O measurements. The general approach can also be used for other specific goals allowing to incorporate a-priori information about the solution with Poisson distributed data.

Significance: We show how small-scale (less than millimeters ² ) neuronal dynamics relates to network activity observed across wide areas (greater than centimeters ² ) during certain network states, such as seizures. Simulations show how macroscopic network properties can affect frequency and amplitude of ictal oscillations. Additionally, the seizure dynamic suggests that one neuronal function, feedforward inhibition, plays different roles across scales: ( i ) inhibition at the small-scale wavefront fails, allowing seizure activity to propagate, but ( ii ) at macroscopic scales, inhibition of the surrounding territory is activated via long-range intracortical connections and creates a distinct pathway to a postictal state. Ultimately, our modeling framework can be used to examine meso- and macroscopic perturbations and evaluate strategies to promote transitions between ictal and nonictal network states.