139 research outputs found
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders
Lesion detection in brain Magnetic Resonance Images (MRI) remains a
challenging task. State-of-the-art approaches are mostly based on supervised
learning making use of large annotated datasets. Human beings, on the other
hand, even non-experts, can detect most abnormal lesions after seeing a handful
of healthy brain images. Replicating this capability of using prior information
on the appearance of healthy brain structure to detect lesions can help
computers achieve human level abnormality detection, specifically reducing the
need for numerous labeled examples and bettering generalization of previously
unseen lesions. To this end, we study detection of lesion regions in an
unsupervised manner by learning data distribution of brain MRI of healthy
subjects using auto-encoder based methods. We hypothesize that one of the main
limitations of the current models is the lack of consistency in latent
representation. We propose a simple yet effective constraint that helps mapping
of an image bearing lesion close to its corresponding healthy image in the
latent space. We use the Human Connectome Project dataset to learn distribution
of healthy-appearing brain MRI and report improved detection, in terms of AUC,
of the lesions in the BRATS challenge dataset.Comment: 9 pages, 5 figures, accepted at MIDL 201
Temporal Interpolation via Motion Field Prediction
Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging enables high
contrast 4D MR imaging during free breathing and provides in-vivo observations
for treatment planning and guidance. Navigator slices are vital for
retrospective stacking of 2D data slices in this method. However, they also
prolong the acquisition sessions. Temporal interpolation of navigator slices an
be used to reduce the number of navigator acquisitions without degrading
specificity in stacking. In this work, we propose a convolutional neural
network (CNN) based method for temporal interpolation via motion field
prediction. The proposed formulation incorporates the prior knowledge that a
motion field underlies changes in the image intensities over time. Previous
approaches that interpolate directly in the intensity space are prone to
produce blurry images or even remove structures in the images. Our method
avoids such problems and faithfully preserves the information in the image.
Further, an important advantage of our formulation is that it provides an
unsupervised estimation of bi-directional motion fields. We show that these
motion fields can be used to halve the number of registrations required during
4D reconstruction, thus substantially reducing the reconstruction time.Comment: Submitted to 1st Conference on Medical Imaging with Deep Learning
(MIDL 2018), Amsterdam, The Netherland
Canonical normalizing flows for manifold learning
Manifold learning flows are a class of generative modelling techniques that
assume a low-dimensional manifold description of the data. The embedding of
such a manifold into the high-dimensional space of the data is achieved via
learnable invertible transformations. Therefore, once the manifold is properly
aligned via a reconstruction loss, the probability density is tractable on the
manifold and maximum likelihood can be used to optimize the network parameters.
Naturally, the lower-dimensional representation of the data requires an
injective-mapping. Recent approaches were able to enforce that the density
aligns with the modelled manifold, while efficiently calculating the density
volume-change term when embedding to the higher-dimensional space. However,
unless the injective-mapping is analytically predefined, the learned manifold
is not necessarily an efficient representation of the data. Namely, the latent
dimensions of such models frequently learn an entangled intrinsic basis, with
degenerate information being stored in each dimension. Alternatively, if a
locally orthogonal and/or sparse basis is to be learned, here coined canonical
intrinsic basis, it can serve in learning a more compact latent space
representation. Toward this end, we propose a canonical manifold learning flow
method, where a novel optimization objective enforces the transformation matrix
to have few prominent and non-degenerate basis functions. We demonstrate that
by minimizing the off-diagonal manifold metric elements -norm, we can
achieve such a basis, which is simultaneously sparse and/or orthogonal.
Canonical manifold flow yields a more efficient use of the latent space,
automatically generating fewer prominent and distinct dimensions to represent
data, and a better approximation of target distributions than other manifold
flow methods in most experiments we conducted, resulting in lower FID scores.Comment: NeurIPS 202
Clinical Prediction from Structural Brain MRI Scans: A Large-Scale Empirical Study
Multivariate pattern analysis (MVPA) methods have become an important tool in neuroimaging, revealing complex associations and yielding powerful prediction models. Despite methodological developments and novel application domains, there has been little effort to compile benchmark results that researchers can reference and compare against. This study takes a significant step in this direction. We employed three classes of state-of-the-art MVPA algorithms and common types of structural measurements from brain Magnetic Resonance Imaging (MRI) scans to predict an array of clinically relevant variables (diagnosis of Alzheimer’s, schizophrenia, autism, and attention deficit and hyperactivity disorder; age, cerebrospinal fluid derived amyloid-β levels and mini-mental state exam score). We analyzed data from over 2,800 subjects, compiled from six publicly available datasets. The employed data and computational tools are freely distributed (https://www.nmr.mgh.harvard.edu/lab/mripredict), making this the largest, most comprehensive, reproducible benchmark image-based prediction experiment to date in structural neuroimaging. Finally, we make several observations regarding the factors that influence prediction performance and point to future research directions. Unsurprisingly, our results suggest that the biological footprint (effect size) has a dramatic influence on prediction performance. Though the choice of image measurement and MVPA algorithm can impact the result, there was no universally optimal selection. Intriguingly, the choice of algorithm seemed to be less critical than the choice of measurement type. Finally, our results showed that cross-validation estimates of performance, while generally optimistic, correlate well with generalization accuracy on a new dataset.BrightFocus Foundation (Alzheimer’s Disease pilot grant (AHAF A2012333))National Institutes of Health (U.S.) (K25 grant (NIBIB 1K25EB013649-01))National Center for Research Resources (U.S.) (U24 RR021382)National Institutes of Health. National Institute for Biomedical Imaging and Bioengineering (R01EB006758)National Institute of Neurological Disorders and Stroke (U.S.) (R01 NS052585-01, 1R21NS072652-01, 1R01NS070963, R01NS083534)National Institutes of Health (U.S.) (Blueprint for Neuroscience Research (5U01-MH093765)
Unsupervised Lesion Detection via Image Restoration with a Normative Prior
Unsupervised lesion detection is a challenging problem that requires
accurately estimating normative distributions of healthy anatomy and detecting
lesions as outliers without training examples. Recently, this problem has
received increased attention from the research community following the advances
in unsupervised learning with deep learning. Such advances allow the estimation
of high-dimensional distributions, such as normative distributions, with higher
accuracy than previous methods.The main approach of the recently proposed
methods is to learn a latent-variable model parameterized with networks to
approximate the normative distribution using example images showing healthy
anatomy, perform prior-projection, i.e. reconstruct the image with lesions
using the latent-variable model, and determine lesions based on the differences
between the reconstructed and original images. While being promising, the
prior-projection step often leads to a large number of false positives. In this
work, we approach unsupervised lesion detection as an image restoration problem
and propose a probabilistic model that uses a network-based prior as the
normative distribution and detect lesions pixel-wise using MAP estimation. The
probabilistic model punishes large deviations between restored and original
images, reducing false positives in pixel-wise detections. Experiments with
gliomas and stroke lesions in brain MRI using publicly available datasets show
that the proposed approach outperforms the state-of-the-art unsupervised
methods by a substantial margin, +0.13 (AUC), for both glioma and stroke
detection. Extensive model analysis confirms the effectiveness of MAP-based
image restoration.Comment: Extended version of 'Unsupervised Lesion Detection via Image
Restoration with a Normative Prior' (MIDL2019
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