Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study.
Med Image Anal
; 18(7): 1217-32, 2014 Oct.
Article
en En
| MEDLINE
| ID: mdl-25113321
ABSTRACT
The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http//vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Interpretación de Imagen Radiográfica Asistida por Computador
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Tomografía Computarizada por Rayos X
/
Pulmón
Tipo de estudio:
Clinical_trials
/
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
País/Región como asunto:
Europa
Idioma:
En
Revista:
Med Image Anal
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2014
Tipo del documento:
Article