volBrain: An Online MRI Brain Volumetry System

Manjón, José V.; Coupé, Pierrick

doi:10.3389/fninf.2016.00030

Cited by 456 publications

(447 citation statements)

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“…volBrain volBrain (http://volbrain.upv.es), which is an open-access platform, is based on an advanced pipeline providing automatic segmentations of several brain structures [20]. The version of volBrain used in the current setting involves an updated version of a recent patch-based method [5].…”

Section: Automatic Segmentationmentioning

confidence: 99%

Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification

et al. 2016

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Section: Automatic Segmentationmentioning

confidence: 99%

Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification

et al. 2016

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“…Each template must be registered to the input volume so that the label fusion stage is carried out within the input coordinates [11]. One of these approaches selects the atlases with the greatest similarity to the input image, aiming to reduce the population average bias [12].…”

Section: Related Workmentioning

confidence: 99%

Multi-atlas label fusion by using supervised local weighting for brain image segmentation

Cárdenas-Peña

Fernández

Ferrández-Vicente

et al. 2017

TecnoL.

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The automatic segmentation of interest structures is devoted to the morphological analysis of brain magnetic resonance imaging volumes. It demands significant efforts due to its complicated shapes and since it lacks contrast between tissues and intersubject anatomical variability. One aspect that reduces the accuracy of the multi-atlasbased segmentation is the label fusion assumption of one-to-one correspondences between targets and atlas voxels. To improve the performance of brain image segmentation, label fusion approaches include spatial and intensity information by using voxel-wise weighted voting strategies. Although the weights are assessed for a predefined atlas set, they are not very efficient for labeling intricate structures since most tissue shapes are not uniformly distributed in the images. This paper proposes a methodology of voxel-wise feature extraction based on the linear combination of patch intensities. As far as we are concerned, this is the first attempt to locally learn the features by maximizing the centered kernel alignment function. Our methodology aims to build discriminative representations, deal with complex structures, and reduce the image artifacts. The result is an enhanced patch-based segmentation of brain images. For validation, the proposed brain image segmentation approach is compared against Bayesian-based and patch-wise label fusion on three different brain image datasets. In terms of the determined Dice similarity index, our proposal shows the highest segmentation accuracy (90.3% on average); it presents sufficient artifact robustness, and provides suitable repeatability of the segmentation results. KeywordsBrain image segmentation, label fusion, multi-atlas segmentation. ResumenLa segmentación automática de estructuras de interés en imágenes de resonancia magnética cerebral requiere esfuerzos significantes, debido a las formas complicadas, el bajo contraste y la variabilidad anatómica. Un aspecto que reduce el desempeño de la segmentación basada en múltiples atlas es la suposición de correspondencias uno-a-uno entre los voxeles objetivo y los del atlas. Para mejorar el desempeño de la segmentación, las metodologías de fusión de etiquetas incluyen información espacial y de intensidad a través de estrategias de votación ponderada a nivel de voxel. Aunque los pesos se calculan para un conjunto de atlas predefinido, estos no son muy eficientes en etiquetar estructuras intrincadas, ya que la mayoría de las formas de los tejidos no se distribuyen uniformemente en las imágenes. Este artículo propone una metodología de extracción de características a nivel de voxel basado en la combinación lineal de las intensidades de un parche. Hasta el momento, este es el primer intento de extraer características locales maximizando la función de alineamiento de kernel centralizado, buscando construir representaciones discriminativas, superar la complejidad de las estructuras, y reducir la influencia de los artefactos. Para validar los resultados, la estrategia de segmentación propuesta ...

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“…Examples of STN visualization scores are shown in Figure 1. The pre-operative planning T1-weighted MRI was used to segment the brain volumes using the volBrain software platform [8]. …”

Section: Methodsmentioning

confidence: 99%

Subthalamic Nucleus Visualization on Routine Clinical Preoperative MRI Scans: A Retrospective Study of Clinical and Image Characteristics Predicting Its Visualization

Ranjan

Boutet

et al. 2018

Stereotact Funct Neurosurg

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Background: The visualization of the subthalamic nucleus (STN) on magnetic resonance imaging (MRI) is variable. Studies of the contribution of patient-related factors and intrinsic brain volumetrics to STN visualization have not been reported previously. Objective: To assess the visualization of the STN during deep brain stimulation (DBS) surgery in a clinical setting. Methods: Eighty-two patients undergoing pre-operative MRI to plan for STN DBS for Parkinson disease were retrospectively studied. The visualization of the STN and its borders was assessed and scored by 3 independent observers using a 4-point ordinal scale (from 0 = not seen to 3 = excellent visualization). This measure was then correlated with the patients’ clinical information and brain volumes. Results: The mean STN visualization scores were 1.68 and 1.63 for the right and left STN, respectively, with a good interobserver reliability (intraclass correlation coefficient: 0.744). Older age and decreased white matter volume were negatively correlated with STN visualization (p < 0.05). Conclusion: STN visualization is only fair to good on routine MRI with good concordance of interindividual rating. Advancing age and decreased white matter are associated with poor visualization of the STN. Knowledge about factors contributing to poor visualization of the STN could alert a surgeon to modify the imaging strategy to optimize surgical targeting.

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volBrain: An Online MRI Brain Volumetry System

Cited by 456 publications

References 50 publications

Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification

Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification

Multi-atlas label fusion by using supervised local weighting for brain image segmentation

Subthalamic Nucleus Visualization on Routine Clinical Preoperative MRI Scans: A Retrospective Study of Clinical and Image Characteristics Predicting Its Visualization

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