10,330 research outputs found
Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast
Partial voluming (PV) is arguably the last crucial unsolved problem in
Bayesian segmentation of brain MRI with probabilistic atlases. PV occurs when
voxels contain multiple tissue classes, giving rise to image intensities that
may not be representative of any one of the underlying classes. PV is
particularly problematic for segmentation when there is a large resolution gap
between the atlas and the test scan, e.g., when segmenting clinical scans with
thick slices, or when using a high-resolution atlas. In this work, we present
PV-SynthSeg, a convolutional neural network (CNN) that tackles this problem by
directly learning a mapping between (possibly multi-modal) low resolution (LR)
scans and underlying high resolution (HR) segmentations. PV-SynthSeg simulates
LR images from HR label maps with a generative model of PV, and can be trained
to segment scans of any desired target contrast and resolution, even for
previously unseen modalities where neither images nor segmentations are
available at training. PV-SynthSeg does not require any preprocessing, and runs
in seconds. We demonstrate the accuracy and flexibility of the method with
extensive experiments on three datasets and 2,680 scans. The code is available
at https://github.com/BBillot/SynthSeg.Comment: accepted for MICCAI 202
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
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
Bridging generative models and Convolutional Neural Networks for domain-agnostic segmentation of brain MRI
Segmentation of brain MRI scans is paramount in neuroimaging, as it is a prerequisite for many subsequent analyses. Although manual segmentation is considered the gold standard, it suffers from severe reproducibility issues, and is extremely tedious, which limits its application to large datasets. Therefore, there is a clear need for automated tools that enable fast and accurate segmentation of brain MRI scans.
Recent methods rely on convolutional neural networks (CNNs). While CNNs obtain accurate results on their training domain, they are highly sensitive to changes in resolution and MRI contrast. Although data augmentation and domain adaptation techniques can increase the generalisability of CNNs, these methods still need to be retrained for every new domain, which requires costly labelling of images.
Here, we present a learning strategy to make CNNs agnostic to MRI contrast, resolution, and numerous artefacts. Specifically, we train a network with synthetic data sampled from a generative model conditioned on segmentations. Crucially, we adopt a domain randomisation approach where all generation parameters are drawn for each example from uniform priors. As a result, the network is forced to learn domain-agnostic features, and can segment real test scans without retraining. The proposed method almost achieves the accuracy of supervised CNNs on their training domain, and substantially outperforms state-of-the-art domain adaptation methods. Finally, based on this learning strategy, we present a segmentation suite for robust analysis of heterogeneous clinical scans. Overall, our approach unlocks the development of morphometry on millions of clinical scans, which ultimately has the potential to improve the diagnosis and characterisation of neurological disorders
Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets
Every year, millions of brain MRI scans are acquired in hospitals, which is a
figure considerably larger than the size of any research dataset. Therefore,
the ability to analyse such scans could transform neuroimaging research. Yet,
their potential remains untapped, since no automated algorithm is robust enough
to cope with the high variability in clinical acquisitions (MR contrasts,
resolutions, orientations, artefacts, subject populations). Here we present
SynthSeg+, an AI segmentation suite that enables, for the first time, robust
analysis of heterogeneous clinical datasets. In addition to whole-brain
segmentation, SynthSeg+ also performs cortical parcellation, intracranial
volume estimation, and automated detection of faulty segmentations (mainly
caused by scans of very low quality). We demonstrate SynthSeg+ in seven
experiments, including an ageing study on 14,000 scans, where it accurately
replicates atrophy patterns observed on data of much higher quality. SynthSeg+
is publicly released as a ready-to-use tool to unlock the potential of
quantitative morphometry.Comment: under review, extension of MICCAI 2022 pape
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