688 research outputs found
Diffeomorphic demons using normalized mutual information, evaluation on multimodal brain MR images
The demons algorithm is a fast non-parametric non-rigid registration method. In recent years great efforts have been made to improve the approach; the state of the art version yields symmetric inverse-consistent largedeformation diffeomorphisms. However, only limited work has explored inter-modal similarity metrics, with no practical evaluation on multi-modality data. We present a diffeomorphic demons implementation using the analytical gradient of Normalised Mutual Information (NMI) in a conjugate gradient optimiser. We report the first qualitative and quantitative assessment of the demons for inter-modal registration. Experiments to spatially normalise real MR images, and to recover simulated deformation fields, demonstrate (i) similar accuracy from NMI-demons and classical demons when the latter may be used, and (ii) similar accuracy for NMI-demons on T1w-T1w and T1w-T2w registration, demonstrating its potential in multi-modal scenarios
Diffeomorphic Demons using Normalised Mutual Information, Evaluation on Multi-Modal Brain MR Images
The demons algorithm is a fast non-parametric non-rigid registration method. In recent years great efforts have been made to improve the approach; the state of the art version yields symmetric inverse-consistent large-deformation diffeomorphisms. However, only limited work has explored inter-modal similarity metrics, with no practical evaluation on multi-modality data. We present a diffeomorphic demons implementation using the analytical gradient of Normalised Mutual Information (NMI) in a conjugate gradient optimiser. We report the first qualitative and quantitative assessment of the demons for inter-modal registration. Experiments to spatially normalise real MR images, and to recover simulated deformation fields, demonstrate (i) similar accuracy from NMI-demons and classical demons when the latter may be used, and (ii) similar accuracy for NMI-demons on T1w-T1w and T1w-T2w registration, demonstrating its potential in multi-modal scenarios
Part-to-whole Registration of Histology and MRI using Shape Elements
Image registration between histology and magnetic resonance imaging (MRI) is
a challenging task due to differences in structural content and contrast. Too
thick and wide specimens cannot be processed all at once and must be cut into
smaller pieces. This dramatically increases the complexity of the problem,
since each piece should be individually and manually pre-aligned. To the best
of our knowledge, no automatic method can reliably locate such piece of tissue
within its respective whole in the MRI slice, and align it without any prior
information. We propose here a novel automatic approach to the joint problem of
multimodal registration between histology and MRI, when only a fraction of
tissue is available from histology. The approach relies on the representation
of images using their level lines so as to reach contrast invariance. Shape
elements obtained via the extraction of bitangents are encoded in a
projective-invariant manner, which permits the identification of common pieces
of curves between two images. We evaluated the approach on human brain
histology and compared resulting alignments against manually annotated ground
truths. Considering the complexity of the brain folding patterns, preliminary
results are promising and suggest the use of characteristic and meaningful
shape elements for improved robustness and efficiency.Comment: Paper accepted at ICCV Workshop (Bio-Image Computing
Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
Segmenting vascular pathologies such as white matter lesions in Brain
magnetic resonance images (MRIs) require acquisition of multiple sequences such
as T1-weighted (T1-w) --on which lesions appear hypointense-- and fluid
attenuated inversion recovery (FLAIR) sequence --where lesions appear
hyperintense--. However, most of the existing retrospective datasets do not
consist of FLAIR sequences. Existing missing modality imputation methods
separate the process of imputation, and the process of segmentation. In this
paper, we propose a method to link both modality imputation and segmentation
using convolutional neural networks. We show that by jointly optimizing the
imputation network and the segmentation network, the method not only produces
more realistic synthetic FLAIR images from T1-w images, but also improves the
segmentation of WMH from T1-w images only.Comment: Conference on Medical Imaging with Deep Learning MIDL 201
Robust training of recurrent neural networks to handle missing data for disease progression modeling
Disease progression modeling (DPM) using longitudinal data is a challenging
task in machine learning for healthcare that can provide clinicians with better
tools for diagnosis and monitoring of disease. Existing DPM algorithms neglect
temporal dependencies among measurements and make parametric assumptions about
biomarker trajectories. In addition, they do not model multiple biomarkers
jointly and need to align subjects' trajectories. In this paper, recurrent
neural networks (RNNs) are utilized to address these issues. However, in many
cases, longitudinal cohorts contain incomplete data, which hinders the
application of standard RNNs and requires a pre-processing step such as
imputation of the missing values. We, therefore, propose a generalized training
rule for the most widely used RNN architecture, long short-term memory (LSTM)
networks, that can handle missing values in both target and predictor
variables. This algorithm is applied for modeling the progression of
Alzheimer's disease (AD) using magnetic resonance imaging (MRI) biomarkers. The
results show that the proposed LSTM algorithm achieves a lower mean absolute
error for prediction of measurements across all considered MRI biomarkers
compared to using standard LSTM networks with data imputation or using a
regression-based DPM method. Moreover, applying linear discriminant analysis to
the biomarkers' values predicted by the proposed algorithm results in a larger
area under the receiver operating characteristic curve (AUC) for clinical
diagnosis of AD compared to the same alternatives, and the AUC is comparable to
state-of-the-art AUCs from a recent cross-sectional medical image
classification challenge. This paper shows that built-in handling of missing
values in LSTM network training paves the way for application of RNNs in
disease progression modeling.Comment: 9 pages, 1 figure, MIDL conferenc
Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling
Disease progression modeling (DPM) using longitudinal data is a challenging
machine learning task. Existing DPM algorithms neglect temporal dependencies
among measurements, make parametric assumptions about biomarker trajectories,
do not model multiple biomarkers jointly, and need an alignment of subjects'
trajectories. In this paper, recurrent neural networks (RNNs) are utilized to
address these issues. However, in many cases, longitudinal cohorts contain
incomplete data, which hinders the application of standard RNNs and requires a
pre-processing step such as imputation of the missing values. Instead, we
propose a generalized training rule for the most widely used RNN architecture,
long short-term memory (LSTM) networks, that can handle both missing predictor
and target values. The proposed LSTM algorithm is applied to model the
progression of Alzheimer's disease (AD) using six volumetric magnetic resonance
imaging (MRI) biomarkers, i.e., volumes of ventricles, hippocampus, whole
brain, fusiform, middle temporal gyrus, and entorhinal cortex, and it is
compared to standard LSTM networks with data imputation and a parametric,
regression-based DPM method. The results show that the proposed algorithm
achieves a significantly lower mean absolute error (MAE) than the alternatives
with p < 0.05 using Wilcoxon signed rank test in predicting values of almost
all of the MRI biomarkers. Moreover, a linear discriminant analysis (LDA)
classifier applied to the predicted biomarker values produces a significantly
larger AUC of 0.90 vs. at most 0.84 with p < 0.001 using McNemar's test for
clinical diagnosis of AD. Inspection of MAE curves as a function of the amount
of missing data reveals that the proposed LSTM algorithm achieves the best
performance up until more than 74% missing values. Finally, it is illustrated
how the method can successfully be applied to data with varying time intervals.Comment: arXiv admin note: substantial text overlap with arXiv:1808.0550
A Multi-Path Approach to Histology Volume Reconstruction
This paper presents a method for correcting erratic pairwise registrations when reconstructing a volume from 2D histology slices. Due to complex and unpredictable alterations of the content of histology images, a pairwise rigid registration between two adjacent slices may fail systematically. Conversely, a neighbouring registration, which potentially involves one of these two slices, will work. This grounds our approach: using correct spatial correspondences established through neighbouring registrations to account for direct failures. We propose to search the best alignment of every couple of adjacent slices from a finite set of transformations that involve neighbouring slices in a transitive fashion. Using the proposed method, we obtained reconstructed volumes with increased coherence compared to the classical pairwise approach, both in synthetic and real data
Longitudinal measurement of the developing grey matter in preterm subjects using multi-modal MRI.
Preterm birth is a major public health concern, with the severity and occurrence of adverse outcome increasing with earlier delivery. Being born preterm disrupts a time of rapid brain development: in addition to volumetric growth, the cortex folds, myelination is occurring and there are changes on the cellular level. These neurological events have been imaged non-invasively using diffusion-weighted (DW) MRI. In this population, there has been a focus on examining diffusion in the white matter, but the grey matter is also critically important for neurological health. We acquired multi-shell high-resolution diffusion data on 12 infants born at ≤28weeks of gestational age at two time-points: once when stable after birth, and again at term-equivalent age. We used the Neurite Orientation Dispersion and Density Imaging model (NODDI) (Zhang et al., 2012) to analyse the changes in the cerebral cortex and the thalamus, both grey matter regions. We showed region-dependent changes in NODDI parameters over the preterm period, highlighting underlying changes specific to the microstructure. This work is the first time that NODDI parameters have been evaluated in both the cortical and the thalamic grey matter as a function of age in preterm infants, offering a unique insight into neuro-development in this at-risk population
Deep Boosted Regression for MR to CT Synthesis
Attenuation correction is an essential requirement of positron emission
tomography (PET) image reconstruction to allow for accurate quantification.
However, attenuation correction is particularly challenging for PET-MRI as
neither PET nor magnetic resonance imaging (MRI) can directly image tissue
attenuation properties. MRI-based computed tomography (CT) synthesis has been
proposed as an alternative to physics based and segmentation-based approaches
that assign a population-based tissue density value in order to generate an
attenuation map. We propose a novel deep fully convolutional neural network
that generates synthetic CTs in a recursive manner by gradually reducing the
residuals of the previous network, increasing the overall accuracy and
generalisability, while keeping the number of trainable parameters within
reasonable limits. The model is trained on a database of 20 pre-acquired MRI/CT
pairs and a four-fold random bootstrapped validation with a 80:20 split is
performed. Quantitative results show that the proposed framework outperforms a
state-of-the-art atlas-based approach decreasing the Mean Absolute Error (MAE)
from 131HU to 68HU for the synthetic CTs and reducing the PET reconstruction
error from 14.3% to 7.2%.Comment: Accepted at SASHIMI201
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