Spectral Forests: Learning of Surface Data, Application to Cortical Parcellation

Lombaert, Hervé; Criminisi, Antonio; Ayache, Nicholas

doi:10.1007/978-3-319-24553-9_67

Cited by 15 publications

(11 citation statements)

References 28 publications

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“…Spectral methods have also been explored for the prediction of visual tasks from MEG signals on a small number of subjects (Guo et al, 2017), while a bootstrapping strategy was used by Anirudh and Thiagarajan (2017) for ASD prediction. Finally, Lombaert et al (2015) combine spectral theory with random forests to process brain surfaces using spectral representations of meshes. Their proposed Spectral Forests are applied to the brain parcellation problem.…”

Section: Graph-based Models For Disease Predictionmentioning

confidence: 99%

Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer’s disease

Parisot¹,

Ktena

Ferrante

et al. 2018

Medical Image Analysis

502

423

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Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical applications, specifically, nodes can represent individuals within a potentially large population (patients or healthy controls) accompanied by a set of features, while the graph edges incorporate associations between subjects in an intuitive manner. This representation allows to incorporate the wealth of imaging and non-imaging information as well as individual subject features simultaneously in disease classification tasks. Previous graph-based approaches for supervised or unsupervised learning in the context of disease prediction solely focus on pairwise similarities between subjects, disregarding individual characteristics and features, or rather rely on subject-specific imaging feature vectors and fail to model interactions between them. In this paper, we present a thorough evaluation of a generic framework that leverages both imaging and non-imaging information and can be used for brain analysis in large populations. This framework exploits Graph Convolutional Networks (GCNs) and involves representing populations as a sparse graph, where its nodes are associated with imaging-based feature vectors, while phenotypic information is integrated as edge weights. The extensive evaluation explores the effect of each individual component of this framework on disease prediction performance and further compares it to different baselines. The framework performance is tested on two large datasets with diverse underlying data, ABIDE and ADNI, for the prediction of Autism Spectrum Disorder and conversion to Alzheimer's disease, respectively. Our analysis shows that our novel framework can improve over state-of-the-art results on both databases, with 70.4% classification accuracy for ABIDE and 80.0% for ADNI.

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Section: Graph-based Models For Disease Predictionmentioning

confidence: 99%

Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer’s disease

Parisot¹,

Ktena

Ferrante

et al. 2018

Medical Image Analysis

502

423

View full text Add to dashboard Cite

show abstract

“…To do so, we compare the classification accuracy on 32 cortical parcels when running our algorithm, respectively, in the Euclidean and Spectral domains. Quantitative results are measured in terms of average Dice overlap and Hausdorff distances [18]. Qualitative results are shown in Fig.…”

Section: Euclidean Versus Spectral Domainmentioning

confidence: 99%

“…The transfer of spectral coordinates across domains provides a robust formulation for spectral methods that naturally handles differences across Laplacian eigenvectors, including sign flips, ordering and mixing of eigenvectors in higher frequencies. This spectral alignment strategy was exploited to learn surface data [18], but was limited to pointwise information, ignoring local patterns within surface neighborhoods.…”

Section: Introductionmentioning

confidence: 99%

Graph Convolutions on Spectral Embeddings for Cortical Surface Parcellation

Gopinath

Desrosiers

Lombaert

2019

Medical Image Analysis

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“…Among them, RF 12 have received increasing attention due to the appealing trade off between efficiency, computational time, and ease of use. For example, RF has been used by Lombaert et al 15 to classify adult cortical surface data represented by their spectral coordinates combined with sulcal depth information. Such anatomical information, and thus the method cannot be reliably applied to fetal data as sulcal features are not stable across ages.…”

Section: Methodsmentioning

confidence: 99%

Unsupervised fetal cortical surface parcellation

Dahdouh

Limperopoulos

2016

SPIE Proceedings

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At the core of many neuro-imaging studies, atlas-based brain parcellations are used for example to study normal brain evolution across the lifespan. These atlases rely on the assumption that the same anatomical features are present on all subjects to be studied and that these features are stable enough to allow meaningful comparisons between different brain surfaces and structures These methods, however, often fail when applied to fetal MRI data, due to the lack of consistent anatomical features present across gestation. This paper presents a novel surface-based fetal cortical parcellation framework which attempts to circumvent the lack of consistent anatomical features by proposing a brain parcellation scheme that is based solely on learned geometrical features. A mesh signature incorporating both extrinsic and intrinsic geometrical features is proposed and used in a clustering scheme to define a parcellation of the fetal brain. This parcellation is then learned using a Random Forest (RF) based learning approach and then further refined in an alpha-expansion graph-cut scheme. Based on the votes obtained by the RF inference procedure, a probability map is computed and used as a data term in the graph-cut procedure. The smoothness term is defined by learning a transition matrix based on the dihedral angles of the faces. Qualitative and quantitative results on a cohort of both healthy and high-risk fetuses are presented. Both visual and quantitative assessments show good results demonstrating a reliable method for fetal brain data and the possibility of obtaining a parcellation of the fetal cortical surfaces using only geometrical features.

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Spectral Forests: Learning of Surface Data, Application to Cortical Parcellation

Cited by 15 publications

References 28 publications

Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer’s disease

Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer’s disease

Graph Convolutions on Spectral Embeddings for Cortical Surface Parcellation

Unsupervised fetal cortical surface parcellation

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