Segmentación computacional de la vena cava superior y procesos hipertensivos

Yoleidy Huérfano, Miguel Vera, Atilio Del Mar, María Vera, José Chacón, Sandra Wilches-Duran, Modesto Graterol-Rivas, Maritza Torres, Víctor Arias, Joselyn Rojas, Carem Prieto, Wilson Siguencia, Lisse Angarita, Rina Ortiz, Diana Rojas-Gomez, Carlos Garicano, Daniela Riaño-Wilches, Maricarmen Chacín, Julio Contreras-Velásquez, Valmore BermúdezAntonio Bravo

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

2 Citas (Scopus)

Resumen

Astrategy for superior vena cava (SVC) three-dimensional segmentation is proposed using 20 cardiac imaging multilayer computed tomography, for entire cardiac cycle of a subject. This strategy is global similarity enhancement technique based on and it comprises of pre-processing, segmentation and parameter tuning stages. The pre-processing stage is split into two phases called filtering and definition of a region of interest. These phases are preliminarily applied to end-diastole cardiac-phase and they address the noise, artifacts and low contrast images problems. During SVC segmentation, the region growing algorithm is applied to the pre-processed images and it is initialized using a voxel detected with least squares support vector machines. During the parameters tuning, the Dice score (Ds) is used to compare the SVC segmentations, obtained by the proposed strategy, and manually SVC segmentation, generated by a cardiologist. The combination of filtering techniques that generated the highest Ds considering the end-diastole phase is then applied to the others 19 3-D images, yielding more than 0.9 average Ds indicating an excellent correlation between the segmentations generated by an expert cardiologist and those produced by the strategy developed.

Título traducido de la contribuciónSuperior vena cava computational segmentation and hypertensive processes
Idioma originalEspañol
Páginas (desde-hasta)25-29
Número de páginas5
PublicaciónRevista Latinoamericana de Hipertension
Volumen11
N.º2
EstadoPublicada - 2016

Palabras clave

  • Global similarity enhancement
  • Segmentation.
  • Superior vena cava

Áreas temáticas de ASJC Scopus

  • Medicina interna
  • Cardiología y medicina cardiovascular

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