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

Fusión de etiquetas basado en múltiples atlas usando ponderaciones locales supervisadas

dc.creatorCárdenas-Peña, David
dc.creatorFernández, Eduardo
dc.creatorFerrández-Vicente, José M.
dc.creatorCastellanos-Domínguez, German
dc.date2017-05-02
dc.date.accessioned2021-03-18T21:06:52Z
dc.date.available2021-03-18T21:06:52Z
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/724
dc.identifier10.22430/22565337.724
dc.identifier.urihttp://test.repositoriodigital.com:8080/handle/123456789/11715
dc.descriptionThe 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.en-US
dc.descriptionLa 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 se compara contra la segmentación Bayesiana y la fusión de etiquetas basada en parches en tres bases de datos diferentes. Respecto del índice de similitud Dice, nuestra propuesta alcanza el más alto acierto (90.3% en promedio) con suficiente robusticidad ante los artefactos y respetabilidad apropiada.es-ES
dc.formatapplication/pdf
dc.languageeng
dc.publisherInstituto Tecnológico Metropolitano (ITM)en-US
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/724/700
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dc.rightshttps://creativecommons.org/licenses/by/3.0/deed.es_ESen-US
dc.sourceTecnoLógicas; Vol. 20 No. 39 (2017); 209-225en-US
dc.sourceTecnoLógicas; Vol. 20 Núm. 39 (2017); 209-225es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectBrain image segmentationen-US
dc.subjectlabel fusionen-US
dc.subjectmulti-atlas segmentationen-US
dc.subjectSegmentación de imágenes cerebraleses-ES
dc.subjectfusión de etiquetases-ES
dc.subjectsegmentación con múltiples atlases-ES
dc.titleMulti-atlas label fusion by using supervised local weighting for brain image segmentationen-US
dc.titleFusión de etiquetas basado en múltiples atlas usando ponderaciones locales supervisadases-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES


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