7 research outputs found
Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning
The crucial components of a conventional image registration method are the
choice of the right feature representations and similarity measures. These two
components, although elaborately designed, are somewhat handcrafted using human
knowledge. To this end, these two components are tackled in an end-to-end
manner via reinforcement learning in this work. Specifically, an artificial
agent, which is composed of a combined policy and value network, is trained to
adjust the moving image toward the right direction. We train this network using
an asynchronous reinforcement learning algorithm, where a customized reward
function is also leveraged to encourage robust image registration. This trained
network is further incorporated with a lookahead inference to improve the
registration capability. The advantage of this algorithm is fully demonstrated
by our superior performance on clinical MR and CT image pairs to other
state-of-the-art medical image registration methods
Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease
Being able to estimate a patient’s progress in the course of Alzheimer’s disease and predicting future progression based on a number of observed biomarker values is of great interest for patients, clinicians and researchers alike. In this work, an approach for disease progress estimation is presented. Based on a set of subjects that convert to a more severe disease stage during the study, models that describe typical trajectories of biomarker values in the course of disease are learned using quantile regression. A novel probabilistic method is then derived to estimate the current disease progress as well as the rate of progression of an individual by fitting acquired biomarkers to the models. A particular strength of the method is its ability to naturally handle missing data. This means, it is applicable even if individual biomarker measurements are missing for a subject without requiring a retraining of the model. The functionality of the presented method is demonstrated using synthetic and—employing cognitive scores and image-based biomarkers—real data from the ADNI study. Further, three possible applications for progress estimation are demonstrated to underline the versatility of the approach: classification, construction of a spatio-temporal disease progression atlas and prediction of future disease progression