176 research outputs found
MRI Super-Resolution using Multi-Channel Total Variation
This paper presents a generative model for super-resolution in routine
clinical magnetic resonance images (MRI), of arbitrary orientation and
contrast. The model recasts the recovery of high resolution images as an
inverse problem, in which a forward model simulates the slice-select profile of
the MR scanner. The paper introduces a prior based on multi-channel total
variation for MRI super-resolution. Bias-variance trade-off is handled by
estimating hyper-parameters from the low resolution input scans. The model was
validated on a large database of brain images. The validation showed that the
model can improve brain segmentation, that it can recover anatomical
information between images of different MR contrasts, and that it generalises
well to the large variability present in MR images of different subjects. The
implementation is freely available at https://github.com/brudfors/spm_superre
An Algorithm for Learning Shape and Appearance Models without Annotations
This paper presents a framework for automatically learning shape and
appearance models for medical (and certain other) images. It is based on the
idea that having a more accurate shape and appearance model leads to more
accurate image registration, which in turn leads to a more accurate shape and
appearance model. This leads naturally to an iterative scheme, which is based
on a probabilistic generative model that is fit using Gauss-Newton updates
within an EM-like framework. It was developed with the aim of enabling
distributed privacy-preserving analysis of brain image data, such that shared
information (shape and appearance basis functions) may be passed across sites,
whereas latent variables that encode individual images remain secure within
each site. These latent variables are proposed as features for
privacy-preserving data mining applications.
The approach is demonstrated qualitatively on the KDEF dataset of 2D face
images, showing that it can align images that traditionally require shape and
appearance models trained using manually annotated data (manually defined
landmarks etc.). It is applied to MNIST dataset of handwritten digits to show
its potential for machine learning applications, particularly when training
data is limited. The model is able to handle ``missing data'', which allows it
to be cross-validated according to how well it can predict left-out voxels. The
suitability of the derived features for classifying individuals into patient
groups was assessed by applying it to a dataset of over 1,900 segmented
T1-weighted MR images, which included images from the COBRE and ABIDE datasets.Comment: 61 pages, 16 figures (some downsampled by a factor of 4), submitted
to MedI
Model-based multi-parameter mapping
Quantitative MR imaging is increasingly favoured for its richer information
content and standardised measures. However, computing quantitative parameter
maps, such as those encoding longitudinal relaxation rate (R1), apparent
transverse relaxation rate (R2*) or magnetisation-transfer saturation (MTsat),
involves inverting a highly non-linear function. Many methods for deriving
parameter maps assume perfect measurements and do not consider how noise is
propagated through the estimation procedure, resulting in needlessly noisy
maps. Instead, we propose a probabilistic generative (forward) model of the
entire dataset, which is formulated and inverted to jointly recover (log)
parameter maps with a well-defined probabilistic interpretation (e.g., maximum
likelihood or maximum a posteriori). The second order optimisation we propose
for model fitting achieves rapid and stable convergence thanks to a novel
approximate Hessian. We demonstrate the utility of our flexible framework in
the context of recovering more accurate maps from data acquired using the
popular multi-parameter mapping protocol. We also show how to incorporate a
joint total variation prior to further decrease the noise in the maps, noting
that the probabilistic formulation allows the uncertainty on the recovered
parameter maps to be estimated. Our implementation uses a PyTorch backend and
benefits from GPU acceleration. It is available at
https://github.com/balbasty/nitorch.Comment: 20 pages, 6 figures, accepted at Medical Image Analysi
Cellular morphometric analysis: from microscopic scale to whole mouse brains
International audienceIn neurodegenerative diseases, pathological aggregates disturb cell function and morphology. Quantifying these changes is of prime interest but raises experimental and computational challenges. In this context, whole-slide imaging (WSI) offers the unique opportunity to analyze whole mouse brain sections at the cellular level using a variety of histological markers. However, this technique generates terabytes of data which is difficult to fully analyze.We developed a novel method enabling: (1) to detect cells and pathological aggregates in WSI color images at the cellular level; (2) to quantify parameters of interest such as density, shape, location or color and (3) to integrate the information within quantitative and multi-scale heat maps.This original approach enables to extract pertinent information from high-resolution qualitative images and to dramatically reduce the amount of information to be processed. A supplementary step of this work consists in extending the analysis from brain sections to the entire brains reconstructed in 3D using our in-house software BrainVISA (http://brainvisa.info).From the generated 3D parametric maps, voxel-wise statistical studies can be performed to investigate cellular structural alterations without a priori. Furthermore, correlating 3D whole-brain parametric maps with in vivo imaging modalities (MRI, fMRI, PET, in vivo microscopy, etc.) will improve the understanding of the relationship between brain structure and function in disease
Fitting Segmentation Networks on Varying Image Resolutions using Splatting
Data used in image segmentation are not always defined on the same grid. This
is particularly true for medical images, where the resolution, field-of-view
and orientation can differ across channels and subjects. Images and labels are
therefore commonly resampled onto the same grid, as a pre-processing step.
However, the resampling operation introduces partial volume effects and
blurring, thereby changing the effective resolution and reducing the contrast
between structures. In this paper we propose a splat layer, which automatically
handles resolution mismatches in the input data. This layer pushes each image
onto a mean space where the forward pass is performed. As the splat operator is
the adjoint to the resampling operator, the mean-space prediction can be pulled
back to the native label space, where the loss function is computed. Thus, the
need for explicit resolution adjustment using interpolation is removed. We show
on two publicly available datasets, with simulated and real multi-modal
magnetic resonance images, that this model improves segmentation results
compared to resampling as a pre-processing step.Comment: Accepted for MIUA 202
Fitting Segmentation Networks on Varying Image Resolutions using Splatting
Data used in image segmentation are not always defined on the same grid. This is particularly true for medical images, where the resolution, field-of-view and orientation can differ across channels and subjects. Images and labels are therefore commonly resampled onto the same grid, as a pre-processing step. However, the resampling operation introduces partial volume effects and blurring, thereby changing the effective resolution and reducing the contrast between structures. In this paper we propose a splat layer, which automatically handles resolution mismatches in the input data. This layer pushes each image onto a mean space where the forward pass is performed. As the splat operator is the adjoint to the resampling operator, the mean-space prediction can be pulled back to the native label space, where the loss function is computed. Thus, the need for explicit resolution adjustment using interpolation is removed. We show on two publicly available datasets, with simulated and real multi-modal magnetic resonance images, that this model improves segmentation results compared to resampling as a pre-processing step
Robust supervised segmentation of neuropathology whole-slide microscopy images
International audienceAlzheimer's disease is characterized by brain pathological aggregates such as Aβ plaques and neurofibrillary tangles which trigger neuroinflammation and participate to neuronal loss. Quantification of these pathological markers on histological sections is widely performed to study the disease and to evaluate new therapies. However, segmentation of neuropathology images presents difficulties inherent to histology (presence of debris, tissue folding, non-specific staining) as well as specific challenges (sparse staining, irregular shape of the lesions). Here, we present a supervised classification approach for the robust pixel-level classification of large neuropathology whole slide images. We propose a weighted form of Random Forest in order to fit nonlinear decision boundaries that take into account class imbalance. Both color and texture descriptors were used as predictors and model selection was performed via a leave-one-image-out cross-validation scheme. Our method showed superior results compared to the current state of the art method when applied to the segmentation of Aβ plaques and neurofibrillary tangles in a human brain sample. Furthermore, using parallel computing, our approach easily scales-up to large gigabyte-sized images. To show this, we segmented a whole brain histology dataset of a mouse model of Alzheimer's disease. This demonstrates our method relevance as a routine tool for whole slide microscopy images analysis in clinical and preclinical research settings
Automated cell individualization and counting in cerebral microscopic images
International audienceIn biomedical research, cell counting is important to assess physiological and pathophysiological information. However, the automated analysis of microscopic images of tissues remains extremely challenging. We propose an automated processing protocol for proper segmentation of individual cells in microscopic images. A Gaussian filter is applied to improve signal to noise ratio (SNR) then an original minmax method is proposed to produce an image in which information describing both cell centers (minima) and boundaries are enhanced. Finally, a contour-based model initialized from minima in the min-max cartography is carried out to achieve cell individualization. This method is evaluated on a NeuN-stained macaque brain section in sub-regions presenting various levels of fraction of neuron surface occupation. Comparison with several methods of reference demonstrates that the performances of our method are superior. A first application to the segmentation of neurons in the hippocampus illustrates the ability of our approach to deal with massive and complex data
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