Entangled decision forests and their application for semantic segmentation of CT images.
Inf Process Med Imaging
; 22: 184-96, 2011.
Article
en En
| MEDLINE
| ID: mdl-21761656
ABSTRACT
This work addresses the challenging problem of simultaneously segmenting multiple anatomical structures in highly varied CT scans. We propose the entangled decision forest (EDF) as a new discriminative classifier which augments the state of the art decision forest, resulting in higher prediction accuracy and shortened decision time. Our main contribution is two-fold. First, we propose entangling the binary tests applied at each tree node in the forest, such that the test result can depend on the result of tests applied earlier in the same tree and at image points offset from the voxel to be classified. This is demonstrated to improve accuracy and capture long-range semantic context. Second, during training, we propose injecting randomness in a guided way, in which node feature types and parameters are randomly drawn from a learned (nonuniform) distribution. This further improves classification accuracy. We assess our probabilistic anatomy segmentation technique using a labeled database of CT image volumes of 250 different patients from various scan protocols and scanner vendors. In each volume, 12 anatomical structures have been manually segmented. The database comprises highly varied body shapes and sizes, a wide array of pathologies, scan resolutions, and diverse contrast agents. Quantitative comparisons with state of the art algorithms demonstrate both superior test accuracy and computational efficiency.
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Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Reconocimiento de Normas Patrones Automatizadas
/
Inteligencia Artificial
/
Interpretación de Imagen Radiográfica Asistida por Computador
/
Tomografía Computarizada por Rayos X
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Inf Process Med Imaging
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
/
INFORMATICA MEDICA
Año:
2011
Tipo del documento:
Article
País de afiliación:
Estados Unidos