209 research outputs found
Placental Flattening via Volumetric Parameterization
We present a volumetric mesh-based algorithm for flattening the placenta to a
canonical template to enable effective visualization of local anatomy and
function. Monitoring placental function in vivo promises to support pregnancy
assessment and to improve care outcomes. We aim to alleviate visualization and
interpretation challenges presented by the shape of the placenta when it is
attached to the curved uterine wall. To do so, we flatten the volumetric mesh
that captures placental shape to resemble the well-studied ex vivo shape. We
formulate our method as a map from the in vivo shape to a flattened template
that minimizes the symmetric Dirichlet energy to control distortion throughout
the volume. Local injectivity is enforced via constrained line search during
gradient descent. We evaluate the proposed method on 28 placenta shapes
extracted from MRI images in a clinical study of placental function. We achieve
sub-voxel accuracy in mapping the boundary of the placenta to the template
while successfully controlling distortion throughout the volume. We illustrate
how the resulting mapping of the placenta enhances visualization of placental
anatomy and function. Our code is freely available at
https://github.com/mabulnaga/placenta-flattening .Comment: MICCAI 201
A Redox-Responsive Hyaluronic Acid-Based Hydrogel for Chronic Wound Management
Polymer-based hydrogels have been widely applied for chronic wound therapeutics, due to their well-acclaimed wound exudate management capability. At the same time, there is still an unmet clinical need for simple wound diagnostic tools to assist clinical decision-making at the point of care and deliver on the vision of patient-personalised wound management. To explore this challenge, we present a one-step synthetic strategy to realise a redox-responsive, hyaluronic acid (HA)-based hydrogel that is sensitive to wound environment-related variations in glutathione (GSH) concentration. By selecting aminoethyl disulfide (AED) as a GSH-sensitive crosslinker and considering GSH concentration variations in active and non-self-healing wounds, we investigated the impact of GSH induced AED cleavage on hydrogel dimensions, aiming to build GSH-size relationships for potential point-of-care wound diagnosis. The hydrogel was also found to be non-cytotoxic and aided L929 fibroblast growth and proliferation over seven days in vitro. Such a material offers a very low-cost tool for the visual detection of a target analyte that varies dependent on the status of the cells and tissues (wound detection), and may be further exploited as an implant for fibroblast growth and tissue regeneration (wound repair)
Disease Knowledge Transfer across Neurodegenerative Diseases
We introduce Disease Knowledge Transfer (DKT), a novel technique for
transferring biomarker information between related neurodegenerative diseases.
DKT infers robust multimodal biomarker trajectories in rare neurodegenerative
diseases even when only limited, unimodal data is available, by transferring
information from larger multimodal datasets from common neurodegenerative
diseases. DKT is a joint-disease generative model of biomarker progressions,
which exploits biomarker relationships that are shared across diseases. Our
proposed method allows, for the first time, the estimation of plausible,
multimodal biomarker trajectories in Posterior Cortical Atrophy (PCA), a rare
neurodegenerative disease where only unimodal MRI data is available. For this
we train DKT on a combined dataset containing subjects with two distinct
diseases and sizes of data available: 1) a larger, multimodal typical AD (tAD)
dataset from the TADPOLE Challenge, and 2) a smaller unimodal Posterior
Cortical Atrophy (PCA) dataset from the Dementia Research Centre (DRC), for
which only a limited number of Magnetic Resonance Imaging (MRI) scans are
available. Although validation is challenging due to lack of data in PCA, we
validate DKT on synthetic data and two patient datasets (TADPOLE and PCA
cohorts), showing it can estimate the ground truth parameters in the simulation
and predict unseen biomarkers on the two patient datasets. While we
demonstrated DKT on Alzheimer's variants, we note DKT is generalisable to other
forms of related neurodegenerative diseases. Source code for DKT is available
online: https://github.com/mrazvan22/dkt.Comment: accepted at MICCAI 2019, 13 pages, 5 figures, 2 table
Bypass Enhancement RGB Stream Model for Pedestrian Action Recognition of Autonomous Vehicles
Pedestrian action recognition and intention prediction is one of the core
issues in the field of autonomous driving. In this research field, action
recognition is one of the key technologies. A large number of scholars have
done a lot of work to im-prove the accuracy of the algorithm for the task.
However, there are relatively few studies and improvements in the computational
complexity of algorithms and sys-tem real-time. In the autonomous driving
application scenario, the real-time per-formance and ultra-low latency of the
algorithm are extremely important evalua-tion indicators, which are directly
related to the availability and safety of the au-tonomous driving system. To
this end, we construct a bypass enhanced RGB flow model, which combines the
previous two-branch algorithm to extract RGB feature information and optical
flow feature information respectively. In the train-ing phase, the two branches
are merged by distillation method, and the bypass enhancement is combined in
the inference phase to ensure accuracy. The real-time behavior of the behavior
recognition algorithm is significantly improved on the premise that the
accuracy does not decrease. Experiments confirm the superiority and
effectiveness of our algorithm.Comment: Accepted to ACPR 2019 - Workshop on Computer Vision for Modern
Vehicle
AnyStar: Domain randomized universal star-convex 3D instance segmentation
Star-convex shapes arise across bio-microscopy and radiology in the form of
nuclei, nodules, metastases, and other units. Existing instance segmentation
networks for such structures train on densely labeled instances for each
dataset, which requires substantial and often impractical manual annotation
effort. Further, significant reengineering or finetuning is needed when
presented with new datasets and imaging modalities due to changes in contrast,
shape, orientation, resolution, and density. We present AnyStar, a
domain-randomized generative model that simulates synthetic training data of
blob-like objects with randomized appearance, environments, and imaging physics
to train general-purpose star-convex instance segmentation networks. As a
result, networks trained using our generative model do not require annotated
images from unseen datasets. A single network trained on our synthesized data
accurately 3D segments C. elegans and P. dumerilii nuclei in fluorescence
microscopy, mouse cortical nuclei in micro-CT, zebrafish brain nuclei in EM,
and placental cotyledons in human fetal MRI, all without any retraining,
finetuning, transfer learning, or domain adaptation. Code is available at
https://github.com/neel-dey/AnyStar.Comment: Code available at https://github.com/neel-dey/AnySta
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Detection and analysis of statistical differences in anatomical shape
We present a computational framework for image-based analysis and interpretation of statistical differences in anatomical shape between populations. Applications of such analysis include understanding developmental and anatomical aspects of disorders when comparing patients vs. normal controls, studying morphological changes caused by aging, or even differences in normal anatomy, for example, differences between genders. Once a quantitative description of organ shape is extracted from input images, the problem of identifying differences between the two groups can be reduced to one of the classical questions in machine learning of constructing a classifier function for assigning new examples to one of the two groups while making as few misclassifications as possible. The resulting classifier must be interpreted in terms of shape differences between the two groups back in the image domain. We demonstrate a novel approach to such interpretation that allows us to argue about the identified shape differences in anatomically meaningful terms of organ deformation. Given a classifier function in the feature space, we derive a deformation that corresponds to the differences between the two classes while ignoring shape variability within each class. Based on this approach, we present a system for statistical shape analysis using distance transforms for shape representation and the Support Vector Machines learning algorithm for the optimal classifier estimation and demonstrate it on artificially generated data sets, as well as real medical studies
SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry
Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals across the world. These have the potential to revolutionize our understanding of many neurological diseases, but their morphometric analysis has not yet been possible due to their anisotropic resolution. We present an artificial intelligence technique, "SynthSR," that takes clinical brain MRI scans with any MR contrast (T1, T2, etc.), orientation (axial/coronal/sagittal), and resolution and turns them into high-resolution T1 scans that are usable by virtually all existing human neuroimaging tools. We present results on segmentation, registration, and atlasing of >10,000 scans of controls and patients with brain tumors, strokes, and Alzheimer's disease. SynthSR yields morphometric results that are very highly correlated with what one would have obtained with high-resolution T1 scans. SynthSR allows sample sizes that have the potential to overcome the power limitations of prospective research studies and shed new light on the healthy and diseased human brain
Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series
We present a method for fast biomedical image atlas construction using neural
fields. Atlases are key to biomedical image analysis tasks, yet conventional
and deep network estimation methods remain time-intensive. In this preliminary
work, we frame subject-specific atlas building as learning a neural field of
deformable spatiotemporal observations. We apply our method to learning
subject-specific atlases and motion stabilization of dynamic BOLD MRI
time-series of fetuses in utero. Our method yields high-quality atlases of
fetal BOLD time-series with 5-7 faster convergence compared to
existing work. While our method slightly underperforms well-tuned baselines in
terms of anatomical overlap, it estimates templates significantly faster, thus
enabling rapid processing and stabilization of large databases of 4D dynamic
MRI acquisitions. Code is available at
https://github.com/Kidrauh/neural-atlasingComment: 6 pages, 2 figures. Accepted by Medical Imaging Meets NeurIPS 202
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