Towards segmentation and spatial alignment of the human embryonic brain using deep learning for atlas-based registration

We propose an unsupervised deep learning method for atlas based registration to achieve segmentation and spatial alignment of the embryonic brain in a single framework. Our approach consists of two sequential networks with a specifically designed loss function to address the challenges in 3D first trimester ultrasound. The first part learns the affine transformation and the second part learns the voxelwise nonrigid deformation between the target image and the atlas. We trained this network end-to-end and validated it against a ground truth on synthetic datasets designed to resemble the challenges present in 3D first trimester ultrasound. The method was tested on a dataset of human embryonic ultrasound volumes acquired at 9 weeks gestational age, which showed alignment of the brain in some cases and gave insight in open challenges for the proposed method. We conclude that our method is a promising approach towards fully automated spatial alignment and segmentation of embryonic brains in 3D ultrasound

Instantiated mixed effects modeling of Alzheimer's disease markers

The assessment and prediction of a subject's current and future risk of developing neurodegenerative diseases like Alzheimer's disease are of great interest in both the design of clinical trials as well as in clinical decision making. Exploring the longitudinal trajectory of markers related to neurodegeneration is an important task when selecting subjects for treatment in trials and the clinic, in the evaluation of early disease indicators and the monitoring of disease progression. Given that there is substantial intersubject variability, models that attempt to describe marker trajectories for a whole population will likely lack specificity for the representation of individual patients. Therefore, we argue here that individualized models provide a more accurate alternative that can be used for tasks such as population stratification and a subject-specific prognosis. In the work presented here, mixed effects modeling is used to derive global and individual marker trajectories for a training population. Test subject (new patient) specific models are then instantiated using a stratified “marker signature” that defines a subpopulation of similar cases within the training database. From this subpopulation, personalized models of the expected trajectory of several markers are subsequently estimated for unseen patients. These patient specific models of markers are shown to provide better predictions of time-to-conversion to Alzheimer's disease than population based models

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

Abdomen segmentation in 3D fetal ultrasound using CNN-powered deformable models

In this paper, voxel probability maps generated by a novel fovea fully convolutional network architecture (FovFCN) are used as additional feature images in the context of a segmentation approach based on deformable shape models. The method is applied to fetal 3D ultrasound image data aiming at a segmentation of the abdominal outline of the fetal torso. This is of interest, e.g., for measuring the fetal abdominal circumference, a standard biometric measure in prenatal screening. The method is trained on 126 3D ultrasound images and tested on 30 additional scans. The results show that the approach can successfully combine the advantages of FovFCNs and deformable shape models in the context of challenging image data, such as given by fetal ultrasound. With a mean error of 2.24 mm, the combination of model-based segmentation and neural networks outperforms the separate approaches

Abdomen segmentation in 3D fetal ultrasound using CNN-powered deformable models

In this paper, voxel probability maps generated by a novel fovea fully convolutional network architecture (FovFCN) are used as additional feature images in the context of a segmentation approach based on deformable shape models. The method is applied to fetal 3D ultrasound image data aiming at a segmentation of the abdominal outline of the fetal torso. This is of interest, e.g., for measuring the fetal abdominal circumference, a standard biometric measure in prenatal screening. The method is trained on 126 3D ultrasound images and tested on 30 additional scans. The results show that the approach can successfully combine the advantages of FovFCNs and deformable shape models in the context of challenging image data, such as given by fetal ultrasound. With a mean error of 2.24 mm, the combination of model-based segmentation and neural networks outperforms the separate approaches

Image registration with sliding motion constraints for 4D CT motion correction

A common assumption in medical image registration is that the estimation of a globally continuous deformation field is plausible in reality. However, a sliding behavior of adjacent organ boundaries (e.g. lung and ribcage) cannot be described in a plausible way by a continuous deformation field. In this paper, we address this issue with a novel registration framework that explicitly models sliding of interfaces and can preserve discontinuities in the deformation field along predefined organ boundaries. Incorporated methods involve constrained nonlinear registration and a finite element discretization on unstructured tetrahedral meshes. Evaluation is based on the freely available DIR-Lab datasets