516 research outputs found
Joint Reconstruction and Parcellation of Cortical Surfaces
The reconstruction of cerebral cortex surfaces from brain MRI scans is
instrumental for the analysis of brain morphology and the detection of cortical
thinning in neurodegenerative diseases like Alzheimer's disease (AD). Moreover,
for a fine-grained analysis of atrophy patterns, the parcellation of the
cortical surfaces into individual brain regions is required. For the former
task, powerful deep learning approaches, which provide highly accurate brain
surfaces of tissue boundaries from input MRI scans in seconds, have recently
been proposed. However, these methods do not come with the ability to provide a
parcellation of the reconstructed surfaces. Instead, separate
brain-parcellation methods have been developed, which typically consider the
cortical surfaces as given, often computed beforehand with FreeSurfer. In this
work, we propose two options, one based on a graph classification branch and
another based on a novel generic 3D reconstruction loss, to augment
template-deformation algorithms such that the surface meshes directly come with
an atlas-based brain parcellation. By combining both options with two of the
latest cortical surface reconstruction algorithms, we attain highly accurate
parcellations with a Dice score of 90.2 (graph classification branch) and 90.4
(novel reconstruction loss) together with state-of-the-art surfaces.Comment: accepted at MLCN workshop 202
Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation
In this paper, we present an automated approach for segmenting multiple
sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our
method is based on a deep end-to-end 2D convolutional neural network (CNN) for
slice-based segmentation of 3D volumetric data. The proposed CNN includes a
multi-branch downsampling path, which enables the network to encode information
from multiple modalities separately. Multi-scale feature fusion blocks are
proposed to combine feature maps from different modalities at different stages
of the network. Then, multi-scale feature upsampling blocks are introduced to
upsize combined feature maps to leverage information from lesion shape and
location. We trained and tested the proposed model using orthogonal plane
orientations of each 3D modality to exploit the contextual information in all
directions. The proposed pipeline is evaluated on two different datasets: a
private dataset including 37 MS patients and a publicly available dataset known
as the ISBI 2015 longitudinal MS lesion segmentation challenge dataset,
consisting of 14 MS patients. Considering the ISBI challenge, at the time of
submission, our method was amongst the top performing solutions. On the private
dataset, using the same array of performance metrics as in the ISBI challenge,
the proposed approach shows high improvements in MS lesion segmentation
compared with other publicly available tools.Comment: This paper has been accepted for publication in NeuroImag
A Data-Driven Approach to Morphogenesis under Structural Instability
Morphological development into evolutionary patterns under structural
instability is ubiquitous in living systems and often of vital importance for
engineering structures. Here we propose a data-driven approach to understand
and predict their spatiotemporal complexities. A machine-learning framework is
proposed based on the physical modeling of morphogenesis triggered by internal
or external forcing. Digital libraries of structural patterns are constructed
from the simulation data, which are then used to recognize the abnormalities,
predict their development, and assist in risk assessment and prognosis. The
capabilities to identify the key bifurcation characteristics and predict the
history-dependent development from the global and local features are
demonstrated by examples of brain growth and aerospace structural design, which
offer guidelines for disease diagnosis/prognosis and instability-tolerant
design
Improvement in Alzheimer's Disease MRI Images Analysis by Convolutional Neural Networks Via Topological Optimization
This research underscores the efficacy of Fourier topological optimization in
refining MRI imagery, thereby bolstering the classification precision of
Alzheimer's Disease through convolutional neural networks. Recognizing that MRI
scans are indispensable for neurological assessments, but frequently grapple
with issues like blurriness and contrast irregularities, the deployment of
Fourier topological optimization offered enhanced delineation of brain
structures, ameliorated noise, and superior contrast. The applied techniques
prioritized boundary enhancement, contrast and brightness adjustments, and
overall image lucidity. Employing CNN architectures VGG16, ResNet50,
InceptionV3, and Xception, the post-optimization analysis revealed a marked
elevation in performance. Conclusively, the amalgamation of Fourier topological
optimization with CNNs delineates a promising trajectory for the nuanced
classification of Alzheimer's Disease, portending a transformative impact on
its diagnostic paradigms
Multidirectional and Topography-based Dynamic-scale Varifold Representations with Application to Matching Developing Cortical Surfaces
The human cerebral cortex is marked by great complexity as well as substantial dynamic changes during early postnatal development. To obtain a fairly comprehensive picture of its age-induced and/or disorder-related cortical changes, one needs to match cortical surfaces to one another, while maximizing their anatomical alignment. Methods that geodesically shoot surfaces into one another as currents (a distribution of oriented normals) and varifolds (a distribution of non-oriented normals) provide an elegant Riemannian framework for generic surface matching and reliable statistical analysis. However, both conventional current and varifold matching methods have two key limitations. First, they only use the normals of the surface to measure its geometry and guide the warping process, which overlooks the importance of the orientations of the inherently convoluted cortical sulcal and gyral folds. Second, the ‘conversion’ of a surface into a current or a varifold operates at a fixed scale under which geometric surface details will be neglected, which ignores the dynamic scales of cortical foldings. To overcome these limitations and improve varifold-based cortical surface registration, we propose two different strategies. The first strategy decomposes each cortical surface into its normal and tangent varifold representations, by integrating principal curvature direction field into the varifold matching framework, thus providing rich information of the orientation of cortical folding and better characterization of the complex cortical geometry. The second strategy explores the informative cortical geometric features to perform a dynamic-scale measurement of the cortical surface that depends on the local surface topography (e.g., principal curvature), thereby we introduce the concept of a topography-based dynamic-scale varifold. We tested the proposed varifold variants for registering 12 pairs of dynamically developing cortical surfaces from 0 to 6 months of age. Both variants improved the matching accuracy in terms of closeness to the target surface and the goodness of alignment with regional anatomical boundaries, when compared with three state-of-the-art methods: (1) diffeomorphic spectral matching, (2) conventional current-based surface matching, and (3) conventional varifold-based surface matching
Valentino Braitenberg: From neuroanatomy to behavior and back
This article compiles an expose of Valentino Braitenberg's singular view on neuroanatomy and neuroscience. The review emphasizes his topologically informed work on neuroanatomy and his dialectics of brain-based explanations of motor behavior. Some of his early ideas on topologically informed neuroanatomy are presented, together with some of his more obscure work on the taxonomy of neural fiber bundles and synaptic arborizations. His functionally informed interpretations of neuroanatomy of the cerebellum, cortex, and hippocampus, are introduced. Finally, we will touch on his philosophical views and the inextricable role of function in the explanation of neural behavior
- …