A fast right ventricle segmentation in cine-MRI from a dense hough representation

Fabio Martínez-Carrillo; Alejandra Moreno Tarazona; Luis Carlos  Guayacán Chaparro; Lola Xiomara Bautista Rozo; Jean Alejandro  Pico

doi:10.33571/rpolitec.v18n35a6

Authors

Fabio Martínez-Carrillo Doctor en Sistemas y Computación. Docente UIS. Bucaramanga, Colombia, Grupo de Investigación BIVL. Universidad Industrial de Santander (UIS)
Alejandra Moreno Ingeniera de Sistemas. UIS, Universidad Industrial de Santander. Bucaramanga, Colombia. https://orcid.org/0000-0002-2066-6710
Luis Carlos Guayacán Magister en Matemática Aplicada. Estudiante UIS. Bucaramanga, Colombia. Universidad Industrial de Santander
Lola Bautista Doctora en Ciencias. Docente UIS. Bucaramanga, Colombia.. Universidad Industrial de Santander https://orcid.org/0000-0002-3853-007X
Jean Pico Ingeniero de Sistemas. Bucaramanga, Colombia. Universidad Industrial de Santander

DOI:

https://doi.org/10.33571/rpolitec.v18n35a6

Keywords:

Dense Hough Transform, Heart Characterization, RV Segmentation, Heart Disease Classification, Cardiac MRI

Abstract

Segmentation of the right ventricle (RV) is essential for the diagnosis of multiple cardiac pathologies and conditions. However, its manual delineation is a tedious task and computational support is complex due to geometric and dynamic variability. This work introduces a dense Hough transform and representation (HT) that allows a nonparametric characterization of the shape, encoding each voxel by its curvature and orientation. This representation is integrated into a bayesian tracking approach, which efficiently segments the RV structure throughout the cardiac cycle. The proposed approach was evaluated on a public dataset, with 16 patients, achieving a Sørensen-Dice coefficient of 0.87 and 0.92, for complete volumes and basal structures, respectively. These results evidence an adequate fit of the proposed model with respect to RV shape throughout the entire cardiac cycle.

La segmentación del Ventrículo Derecho (VD) es esencial para el diagnóstico de múltiples patologías y condiciones cardiacas. Sin embargo, su delineación manual es una tarea tediosa y el soporte computacional resulta complejo debido a la variabilidad geométrica y dinámica. Este trabajo introduce una transformación y representación densa de Hough (TH) que permite una caracterización no paramétrica de la forma, codificando cada vóxel por su curvatura y orientación. Esta representación es integrada en un enfoque de seguimiento bayesiano, que logra de forma eficiente segmentar la estructura del VD, a lo largo del ciclo cardíaco. El enfoque propuesto fue evaluado en un conjunto de datos públicos, con 16 pacientes, logrando un coeficiente Sørensen-Dice de 0,87 y 0,92, para volúmenes completos y estructuras basales, respectivamente. Estos resultados evidencian una adecuada adaptación del modelo propuesto respecto a la forma del VD a lo largo de todo el ciclo cardíaco.

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Author Biography

Alejandra Moreno, Ingeniera de Sistemas. UIS, Universidad Industrial de Santander. Bucaramanga, Colombia.

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