An automatic technique for left ventricle segmentation from msct cardiac volumes

Bravo

Medina

2022

Data

The World Health Organization indicates the top cause of death is heart disease. These diseases can be detected using several imaging modalities, especially cardiac computed tomography (CT), whose images have imperfections associated with noise and certain artifacts. To minimize the impact of these imperfections on the quality of the CT images, several researchers have developed digital image processing techniques (DPIT) by which the quality is evaluated considering several metrics and databases (DB), both real and simulated. This article describes the processes that made it possible to generate and utilize six three-dimensional synthetic cardiac DBs or voxels-based numerical phantoms. An exhaustive analysis of the most relevant features of images of the left ventricle, belonging to a real CT DB of the human heart, was performed. These features are recreated in the synthetic DBs, generating a reference phantom or ground truth free of imperfections (DB1) and five phantoms, in which Poisson noise (DB2), stair-step artifact (DB3), streak artifact (DB4), both artifacts (DB5) and all imperfections (DB6) are incorporated. These DBs can be used to determine the performance of DPIT, aimed at decreasing the effect of these imperfections on the quality of cardiac images.

Section: Possible Scenarios For the Use Of The Described Phantomsmentioning

confidence: 99%

Description and Use of Three-Dimensional Numerical Phantoms of Cardiac Computed Tomography Images

Bravo

Medina

2022

Data

Use of computational realistic models for the cardiac ejection fraction calculation

Huérfano

Valbuena

et al. 2019

J. Phys.: Conf. Ser.

Ejection fraction is one of the most useful clinical descriptors to determine the cardiac function of a subject. For this reason, obtaining the value of this descriptor is of vital importance and requires high precision. However, in the clinical routine, to generate the mentioned descriptor value, a geometric hypothesis is assumed, obtaining an approximate value for this fraction, usually by excess, and which is a dependent-operator. The aim of the present work is to propose the accurate calculation of the ejection fraction from realistic models, obtained computationally, of the cardiac chamber called right ventricle. Normally, the geometric hypothesis that makes this ventricle coincide with a pyramidal type geometric shape, is not usually, fulfilled in subjects affected by several cardiac pathologies, so as an alternative to this problem, the computational segmentation process is used to generate the morphology of the right ventricle and from it proceeds to obtain, accurately, the ejection fraction value. In this sense, an automatic strategy based on no-lineal filters, smart operator and region growing technique is propose in order to generate the right ventricle ejection fraction. The results are promising due we obtained an excellent correspondence between the manual segmentation and the automatic one generated by the realistic models.

Usefulness of cutting planes in the hierarchical segmentation of cardiac anatomical structures

Valbuena

Huérfano

et al. 2019

J. Phys.: Conf. Ser.

A spatial geometric plane is defined by the three-dimensional coordinates of a pair of spatial points and the direction that the normal vector establishes, which is formed by joining those points by means of an oriented line segment. This type of planes, in three-dimensional images, is extremely useful as an alternative solution to the problem of low contrast that exhibit the anatomical structures present in cardiac computed tomography images. To do this, after using a predetermined filter bank and in order to define a region of interest, a smart operator based on least squares support vector machines is trained and validated in order to detect the aforementioned coordinates which enables the location of the plane, in the three-dimensional space that contains the considered images. Once the structure that is required to segment is identified, a discriminant function is used that cancels all information not linked to this structure. In this work, the segmentation of the left ventricle, based on region growing technique, is firstly considered and then the left atrium is segmented considering region growing technique and an inverse discriminant function. The results show an excellent correspondence relationship when the spatial union of both structures is made.