NABS: non-local automatic brain hemisphere segmentation

"NOTICE: this is the author’s version of a work that was accepted for publication in Magnetic Resonance Imaging. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Magnetic Resonance Imaging, [Volume 33, Issue 4, May 2015, Pages 474–484] DOI 10.1016/j.mri.2015.02.005In this paper, we propose an automatic method to segment the five main brain sub-regions (i.e. left/right hemispheres, left/right cerebellum and brainstem) from magnetic resonance images. The proposed method uses a library of pre-labeled brain images in a stereotactic space in combination with a non-local label fusion scheme for segmentation. The main novelty of the proposed method is the use of a multi-label block-wise label fusion strategy specifically designed to deal with the classification of main brain sub-volumes that process only specific parts of the brain images significantly reducing the computational burden. The proposed method has been quantitatively evaluated against manual segmentations. The evaluation showed that the proposed method was faster while producing more accurate segmentations than a current state-of-the-art method. We also present evidences suggesting that the proposed method was more robust against brain pathologies than the compared method. Finally, we demonstrate the clinical value of our method compared to the state-of-the-art approach in terms of the asymmetry quantification in Alzheimer's disease.We want to thank the OASIS (P50 AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24 RR021382, R01 MH56584) and IXI - Information eXtraction from Images (EPSRC GR/S21533/02) datasets promoters for making available this valuable resource to the scientific community which surely will boost the research in brain imaging. This work has been supported by the Spanish grant TIN2011-26727 from Ministerio de Ciencia e Innovacion. J. Tohka's work was supported by the Academy of Finland grant 130275. This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the Future Programme IdEx Bordeaux (ANR-10-IDEX-03-02), Cluster of Excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57).Romero Gómez, JE.; Manjón Herrera, JV.; Tohka, J.; Coupé, P.; Robles Viejo, M. (2015). NABS: non-local automatic brain hemisphere segmentation. Magnetic Resonance Imaging. 33(4):474-484. https://doi.org/10.1016/j.mri.2015.02.005S47448433

Software para el estudio del volumen de estructuras corticales en imágenes de RMN cerebrales

El estudio del volumen intracraneal requiere del uso de herramientas que permitan objetivar el diagnóstico y ofrezcan un rendimiento y precisión elevados. La segmentación automática del volumen cerebral es el primer paso hacia un estudio más completo del cerebro y supondrá una herramienta versátil en el estudio de diversas patologías. Propósito: Mejorar un método ya implementado que segmenta el volumen cerebral con una calidad aceptable pero en un tiempo de ejecución elevado basado en comparación de regiones contra una biblioteca de casos de ejemplo segmentada manualmente. Método: El método recorre uno a uno todos los voxels del cerebro a segmentar extrayendo la región que lo envuelve y comparándola con regiones de los casos de ejemplo de la biblioteca. Esto era ineficiente así que se han introducido mejoras que van desde la carga y preselección de los casos más semejantes para usarlos en la segmentación hasta introducir una estimación pre calculada del etiquetado de los voxels que no suelen variar para evitar tener que procesarlos. Resultados: Se parte de un método que obtiene segmentaciones con una 98% de fiabilidad y en un tiempo de ejecución de 160 segundos y se ha mejorado hasta una fiabilidad del 99% en un tiempo inferior a 40 segundos.Romero Gómez, JE. (2011). Software para el estudio del volumen de estructuras corticales en imágenes de RMN cerebrales. http://hdl.handle.net/10251/14239.Archivo delegad

Quantification of cerebral grey and white matter asymmetry from MRI

Several studies have reported the presence of morphological di#erences between the brains ofschizophrenic patients and normal controls, such as a decrease of normal cerebral as mmetries. Whilecurrent MR imaging techniques make it feasible to measure brain morpholog in vivo, the significanceof image-derived measurements obtained b slice b slice manual delineation of structures of interestb a trained expert is a#ected b large variabilit and poor reproducibilit . In this paper, we presenta completel automated procedure for quantification of as mmetr in cerebral gre and white mattervolumes from MR images. After bias correction and tissue classification, left and right hemispheresare separated b non-rigid registration to a template image in which both hemispheres have beencarefull segmented. Volume renderings of each hemisphere separatel demonstrate the high qualitof the resulting segmentations. We present quantitative results obtained from a database of MRimages of 40 schizophrenic patients and 31 normal controls. Because all steps in the procedure arecompletel automated and do not require user specified parameters, the results are highl reproducibleand consistent. Statistical anal sis of the left and right white and gre matter volume measurementsand correlation with diagnosis, age, sex and dexterit is in progress.1Internal report KUL/ESAT/PSI/9905, K.U.Leuven, ESAT, April 1999, Leuven, Belgiumstatus: publishe

Quantification of cerebral grey and white matter asymmetry from MRI

We present a completely automated procedure for measuring left and right hemispheric asymmetry in cerebral grey and white matter volumes from NIR images using a chain of state-of-the-art image analysis algorithms. After bias correction and tissue classification, left and right hemispheres are separated by non-rigid registration to a template image in which both hemispheres have been carefully segmented. Volume renderings of each hemisphere separately demonstrate the high quality of the resulting segmentations. Because all steps in the procedure are completely automated and do not require user specified parameters, the results are highly reproducible and consistent. We present quantitative results obtained from a database of NIR images of 40 schizophrenic patients and 31 normal controls.status: publishe

Automated morphometry for mouse brain MRI through structural parcellation and thickness estimation

Quantitative morphometric analysis is an important tool in neuroimaging for the study of understanding the physiology of development, normal aging, disease pathology and treatment effect. However, compared to clinical study, image analysis methods specific to preclinical neuroimaging are still lacking. The aim of this PhD thesis is to achieve automatic quantitative structural analysis of mouse brain MRI. This thesis focuses on two quantitative methods which have been widely accepted as quantitative imaging biomarkers: brain structure segmentation and cortical thickness estimation. Firstly, a multi-atlas based structural parcellation framework has been constructed, which incorporates preprocessing steps such as intensity non-uniformity correction and multi-atlas based brain extraction, followed by non-rigid registration and local weighted multi-atlas label fusion. Validation of the framework demonstrated improved performance compared to single-atlas-based structural parcellation, as well as to global weighted multi-atlas label fusion methods. The framework has been further applied to in vivo and ex vivo data acquired from the same cohort so that the respective volumetric analysis can be compared. The results reveal a non-uniform distribution of volume changes from the in vivo to the post-mortem brain. In addition, volumetric analysis based on the segmented structures showed similar statistical power on in vivo or ex vivo data within the same cohort. Secondly, a framework to segment the mouse cerebellar cortex sublayers from brain MRI data and estimate the thickness of the corresponding layers has been developed. Application of the framework on the experimental data demonstrated its ability to distinguish sublayer thickness variation between transgenic strains and their wild-type littermate, which cannot be detected using full cortical thickness measurements alone. In conclusion, two quantitative morphometric analysis frameworks have been pre-sented in this thesis. This demonstrated the successful application of translational quantitative methods to preclinical mouse brain MRI