An Optimized PatchMatch for multi-scale and multi-feature label fusion
GIRAUD, Rémi
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Institut Polytechnique de Bordeaux [Bordeaux INP]
Institut de Mathématiques de Bordeaux [IMB]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Institut Polytechnique de Bordeaux [Bordeaux INP]
Institut de Mathématiques de Bordeaux [IMB]
TA, Vinh-Thong
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Institut Polytechnique de Bordeaux [Bordeaux INP]
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Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Institut Polytechnique de Bordeaux [Bordeaux INP]
GIRAUD, Rémi
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Institut Polytechnique de Bordeaux [Bordeaux INP]
Institut de Mathématiques de Bordeaux [IMB]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Institut Polytechnique de Bordeaux [Bordeaux INP]
Institut de Mathématiques de Bordeaux [IMB]
TA, Vinh-Thong
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Institut Polytechnique de Bordeaux [Bordeaux INP]
< Leer menos
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Institut Polytechnique de Bordeaux [Bordeaux INP]
Idioma
en
Article de revue
Este ítem está publicado en
NeuroImage. 2016, vol. 124, p. 770-782
Elsevier
Resumen en inglés
Automatic segmentation methods are important tools for quantitative analysis of Magnetic Resonance Images (MRI). Recently, patch-based label fusion approaches have demonstrated state-of-the-art segmentation accuracy. In ...Leer más >
Automatic segmentation methods are important tools for quantitative analysis of Magnetic Resonance Images (MRI). Recently, patch-based label fusion approaches have demonstrated state-of-the-art segmentation accuracy. In this paper, we introduce a new patch-based label fusion framework to perform segmentation of anatomical structures. The proposed approach uses an Optimized PAtchMatch Label fusion (OPAL) strategy that drastically reduces the computation time required for the search of similar patches. The reduced computation time of OPAL opens the way for new strategies and facilitates processing on large databases. In this paper, we investigate new perspectives offered by OPAL, by introducing a new multi-scale and multi-feature framework. During our validation on hippocampus segmentation we use two datasets: young adults in the ICBM cohort and elderly adults in the EADC-ADNI dataset. For both, OPAL is compared to state-of-the-art methods. Results show that OPAL obtained the highest median Dice coefficient (89.9% for ICBM and 90.1% for EADC-ADNI). Moreover, in both cases, OPAL produced a segmentation accuracy similar to inter-expert variability. On the EADC-ADNI dataset, we compare the hippocampal volumes obtained by manual and automatic segmentation. The volumes appear to be highly correlated that enables to perform more accurate separation of pathological populations.< Leer menos
Palabras clave en inglés
Segmentation
Patch-Based Method
Patch Matching
Late Fusion
Hippocampus
Proyecto ANR
Initiative d'excellence de l'Université de Bordeaux - ANR-10-IDEX-0003
Orígen
Importado de HalCentros de investigación