Pieces-of-parts for supervoxel segmentation with global context: Application to DCE-MRI tumour delineation.
Med Image Anal
; 32: 69-83, 2016 08.
Article
en En
| MEDLINE
| ID: mdl-27054278
ABSTRACT
Rectal tumour segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is a challenging task, and an automated and consistent method would be highly desirable to improve the modelling and prediction of patient outcomes from tissue contrast enhancement characteristics - particularly in routine clinical practice. A framework is developed to automate DCE-MRI tumour segmentation, by introducing perfusion-supervoxels to over-segment and classify DCE-MRI volumes using the dynamic contrast enhancement characteristics; and the pieces-of-parts graphical model, which adds global (anatomic) constraints that further refine the supervoxel components that comprise the tumour. The framework was evaluated on 23 DCE-MRI scans of patients with rectal adenocarcinomas, and achieved a voxelwise area-under the receiver operating characteristic curve (AUC) of 0.97 compared to expert delineations. Creating a binary tumour segmentation, 21 of the 23 cases were segmented correctly with a median Dice similarity coefficient (DSC) of 0.63, which is close to the inter-rater variability of this challenging task. A second study is also included to demonstrate the method's generalisability and achieved a DSC of 0.71. The framework achieves promising results for the underexplored area of rectal tumour segmentation in DCE-MRI, and the methods have potential to be applied to other DCE-MRI and supervoxel segmentation problems.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Neoplasias del Recto
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Imagen por Resonancia Magnética
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Interpretación de Imagen Asistida por Computador
Tipo de estudio:
Prognostic_studies
Límite:
Female
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Humans
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Male
Idioma:
En
Revista:
Med Image Anal
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2016
Tipo del documento:
Article