Hierarchical spectral clustering reveals brain size and shape changes in asymptomatic carriers of .

Traditional methods for detecting asymptomatic brain changes in neurodegenerative diseases such as Alzheimer's disease or frontotemporal degeneration typically evaluate changes in volume at a predefined level of granularity, e.g. voxel-wise or in a priori defined cortical volumes of interest.

MeVisLab-OpenVR prototyping platform for virtual reality medical applications.

Purpose: Virtual reality (VR) can provide an added value for diagnosis and/or intervention planning. Several VR software implementations have been proposed but they are often application dependent. Previous attempts for a more generic solution incorporating VR in medical prototyping software (MeVisLab) were still lacking functionality precluding easy and flexible development.

Prediction of Stroke Infarct Growth Rates by Baseline Perfusion Imaging.

Background And Purpose: Computed tomography perfusion imaging allows estimation of tissue status in patients with acute ischemic stroke. We aimed to improve prediction of the final infarct and individual infarct growth rates using a deep learning approach.

Methods: We trained a deep neural network to predict the final infarct volume in patients with acute stroke presenting with large vessel occlusions based on the native computed tomography perfusion images, time to reperfusion and reperfusion status in a derivation cohort (MR CLEAN trial [Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands]).

Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty.

The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation. In order to evaluate and compare segmentation methods, the similarity between a segmentation and a predefined ground truth is measured using popular discrete metrics, such as the Dice score. Recent segmentation methods use a differentiable surrogate metric, such as soft Dice, as part of the loss function during the learning phase.

Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning.

CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the perfusion parameters to determine the tissue status. We pursue a data-driven and deconvolution-free approach, where a deep neural network learns to predict the final infarct volume directly from the native CTP images and metadata such as the time parameters and treatment.

Facial recognition from DNA using face-to-DNA classifiers.

Facial recognition from DNA refers to the identification or verification of unidentified biological material against facial images with known identity. One approach to establish the identity of unidentified biological material is to predict the face from DNA, and subsequently to match against facial images. However, DNA phenotyping of the human face remains challenging.

Robust motion correction for cardiac T1 and ECV mapping using a T1 relaxation model approach.

T1 and ECV mapping are quantitative methods for myocardial tissue characterization using cardiac MRI, and are highly relevant for the diagnosis of diffuse myocardial diseases. Since the maps are calculated pixel-by-pixel from a set of MRI images with different T1-weighting, it is critical to assure exact spatial correspondence between these images. However, in practice, different sources of motion e.

ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI.

Performance of models highly depend not only on the used algorithm but also the data set it was applied to. This makes the comparison of newly developed tools to previously published approaches difficult. Either researchers need to implement others' algorithms first, to establish an adequate benchmark on their data, or a direct comparison of new and old techniques is infeasible.

Non-uniformity in pre-insertional Achilles tendon is not influenced by changing knee angle during isometric contractions.

Achilles tendinopathy remains a prevalent condition among recreational and high-level athletes. Mechanical loading has become the gold standard in managing these injuries, but exercises are often generic and prescribed in a "one-size-fits-all" principle. The aim of this study was to evaluate the impact of knee angle changes and different levels of force production on the non-uniform behavior in the Achilles tendon during isometric contractions.

Genome-wide mapping of global-to-local genetic effects on human facial shape.

Genome-wide association scans of complex multipartite traits like the human face typically use preselected phenotypic measures. Here we report a data-driven approach to phenotyping facial shape at multiple levels of organization, allowing for an open-ended description of facial variation while preserving statistical power. In a sample of 2,329 persons of European ancestry, we identified 38 loci, 15 of which replicated in an independent European sample (n = 1,719).

3D Tendon Strain Estimation Using High-frequency Volumetric Ultrasound Images: A Feasibility Study.

Estimation of strain in tendons for tendinopathy assessment is a hot topic within the sports medicine community. It is believed that, if accurately estimated, existing treatment and rehabilitation protocols can be improved and presymptomatic abnormalities can be detected earlier. State-of-the-art studies present inaccurate and highly variable strain estimates, leaving this problem without solution.

Evaluation of tissue displacement and regional strain in the Achilles tendon using quantitative high-frequency ultrasound.

The Achilles tendon has a unique structure-function relationship thanks to its innate hierarchical architecture in combination with the rotational anatomy of the sub-tendons from the triceps surae muscles. Previous research has provided valuable insight in global Achilles tendon mechanics, but limitations with the technique used remain. Furthermore, given the global approach evaluating muscle-tendon junction to insertion, regional differences in tendon mechanical properties might be overlooked.

Convexity-constrained and nonnegativity-constrained spherical factorization in diffusion-weighted imaging.

Diffusion-weighted imaging (DWI) facilitates probing neural tissue structure non-invasively by measuring its hindrance to water diffusion. Analysis of DWI is typically based on generative signal models for given tissue geometry and microstructural properties. In this work, we generalize multi-tissue spherical deconvolution to a blind source separation problem under convexity and nonnegativity constraints.

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI.

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference.

Simultaneous segmentation and anatomical labeling of the cerebral vasculature.

We present a novel algorithm for the simultaneous segmentation and anatomical labeling of the cerebral vasculature. Unlike existing approaches that first attempt to obtain a good segmentation and then perform labeling, we optimize for both by simultaneously taking into account the image evidence and the prior knowledge about the geometry and connectivity of the vasculature. This is achieved by first constructing an overcomplete graph capturing the vasculature, and then selecting and labeling the subset of edges that most likely represents the true vasculature.

Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model.

Diffusion-weighted imaging and tractography provide a unique, non-invasive technique to study the macroscopic structure and connectivity of brain white matter in vivo. Global tractography methods aim at reconstructing the full-brain fiber configuration that best explains the measured data, based on a generative signal model. In this work, we incorporate a multi-shell multi-tissue model based on spherical convolution, into a global tractography framework, which allows to deal with partial volume effects.

HemoVision: An automated and virtual approach to bloodstain pattern analysis.

Bloodstain pattern analysis (BPA) is a subspecialty of forensic sciences, dealing with the analysis and interpretation of bloodstain patterns in crime scenes. The aim of BPA is uncovering new information about the actions that took place in a crime scene, potentially leading to a confirmation or refutation of a suspect's statement. A typical goal of BPA is to estimate the flight paths for a set of stains, followed by a directional analysis in order to estimate the area of origin for the stains.

An investigation of matching symmetry in the human pinnae with possible implications for 3D ear recognition and sound localization.

The human external ears, or pinnae, have an intriguing shape and, like most parts of the human external body, bilateral symmetry is observed between left and right. It is a well-known part of our auditory sensory system and mediates the spatial localization of incoming sounds in 3D from monaural cues due to its shape-specific filtering as well as binaural cues due to the paired bilateral locations of the left and right ears. Another less broadly appreciated aspect of the human pinna shape is its uniqueness from one individual to another, which is on the level of what is seen in fingerprints and facial features.

Simultaneous segmentation and anatomical labeling of the cerebral vasculature.

We present a novel algorithm for the simultaneous segmentation and anatomical labeling of the cerebral vasculature. The method first constructs an overcomplete graph capturing the vasculature. It then selects and labels the subset of edges that most likely represents the true vasculature.

Modeling 3D facial shape from DNA.

Human facial diversity is substantial, complex, and largely scientifically unexplained. We used spatially dense quasi-landmarks to measure face shape in population samples with mixed West African and European ancestry from three locations (United States, Brazil, and Cape Verde). Using bootstrapped response-based imputation modeling (BRIM), we uncover the relationships between facial variation and the effects of sex, genomic ancestry, and a subset of craniofacial candidate genes.

Anatomical labeling of the circle of willis using maximum a posteriori graph matching.

A new method for anatomically labeling the vasculature is presented and applied to the Circle of Willis. Our method converts the segmented vasculature into a graph that is matched with an annotated graph atlas in a maximum a posteriori (MAP) way. The MAP matching is formulated as a quadratic binary programming problem which can be solved efficiently.

Track orientation density imaging (TODI) and track orientation distribution (TOD) based tractography.

Ever since the introduction of the concept of fiber tractography, methods to generate better and more plausible tractograms have become available. Many modern methods can handle complex fiber architecture and take on a probabilistic approach to account for different sources of uncertainty. The resulting tractogram from any such method typically represents a finite random sample from a complex distribution of possible tracks.

A spatially-dense regression study of facial form and tissue depth: towards an interactive tool for craniofacial reconstruction.

Forensic Craniofacial Reconstruction (CFR) is an investigative technique used to illicit recognition of a deceased person by reconstructing the most likely face starting from the skull. A key component in most CFR methods are estimates of facial soft tissue depths (TD) at particular points (landmarks) on the skull based on averages from databases of TD recordings. These databases vary in their method of extraction, number and position of landmarks (usually sparse <100), condition of the body, population studied, and sub-categorization of the data.

Redefining the target early during treatment. Can we visualize regional differences within the target volume using sequential diffusion weighted MRI?

Purpose: In head and neck cancer, diffusion weighted MRI (DWI) can predict response early during treatment. Treatment-induced changes and DWI-specific artifacts hinder an accurate registration between apparent diffusion coefficient (ADC) maps. The aim of the study was to develop a registration tool which calculates and visualizes regional changes in ADC.

Unsupervised segmentation, clustering, and groupwise registration of heterogeneous populations of brain MR images.

Population analysis of brain morphology from magnetic resonance images contributes to the study and understanding of neurological diseases. Such analysis typically involves segmentation of a large set of images and comparisons of these segmentations between relevant subgroups of images (e.g.