Volumetry of subdural hematomas in computed tomography images: ABC methods versus an intelligent computational technique

This work evaluates the performance of some methods oriented towards the generation of the volume of four subdural hematomas (SDH), present in multi-layer computed tomography images. To do this, firstly, a reference volume is specified: the volume obtained by a neurosurgeon using the manual planimet...

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Autores:
Vera, Miguel
Huérfano, Yoleidy
Borrero, Maryury
Valbuena, Oscar
Salazar, Williams
Vera, María Isabel
Barrera, Doris
Hernández, Carlos
Molina, Ángel Valentín
Martínez, Luis Javier
Salazar MSc, Juan
Gelvez, Elkin
Contreras, Yudith
Sáenz, Frank
Tipo de recurso:
Fecha de publicación:
2018
Institución:
Universidad Simón Bolívar
Repositorio:
Repositorio Digital USB
Idioma:
eng
OAI Identifier:
oai:bonga.unisimon.edu.co:20.500.12442/2526
Acceso en línea:
http://hdl.handle.net/20.500.12442/2526
Palabra clave:
ABC Methods
Automatic Intelligent Technique
Segmentation
Volumetry of subdural hematomas
Métodos ABC
Técnica automática inteligente
Segmentación
Volumetría de hematomas subdurales
Rights
License
Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
Description
Summary:This work evaluates the performance of some methods oriented towards the generation of the volume of four subdural hematomas (SDH), present in multi-layer computed tomography images. To do this, firstly, a reference volume is specified: the volume obtained by a neurosurgeon using the manual planimetric method (MPM); which allows the generation of manual segmentations of space-occupying lesions. In this case, these volumes are matched with the SDH. In parallel, the volumetry of the 4 SDHs is obtained, considering both the original version of the ABC/2 method and two of its variants, identified in this paper as ABC/3 method and 2ABC/3 method. The ABC methods allow the calculation of the volume of the hematoma under the assumption that the SDH has an ellipsoidal shape. In third place, SDH’s are studied through an intelligent automatic technique (SAT) that generates the three-dimensional segmentation of each SDH. Finally, the percentage relative error is calculated as a metric to evaluate the methodologies considered. The results show that the SAT method exhibits the best performance generating an average percentage error of less than 5%.