Affiliations: [a]
Department of Computer and Software Engineering, College of EME, National University of Sciences and Technology, Pakistan
| [b]
Department of Embedded Systems Engineering, College of Information Technology, Incheon National University, Korea
Correspondence:
[*]
Corresponding authors. Farhan Hussain, Department of Computer and Software Engineering, College of EME, National University of Sciences and Technology, Pakistan and Gwanggil Jeon, Department of Embedded Systems Engineering, College of Information Technology, Incheon National University, Korea. E-mail: farhan.hussain@ceme.nust.edu.pk (F. Hussain); gjeon@inu.ac.kr (G. Jeon).
Abstract: 3D Cardiac Magnetic Resonance Imaging (MRI) is widely used for the diagnosis of cardiac diseases such as congenital heart defect, left ventricular hypertrophy and left atrium hypertrophy etc. It is one of the noninvasive technique to examine cardiac anatomy. However this technique is semi- automatic, i.e. the images obtained directly from MRI machine have to be segmented manually. This includes the segmentation of chambers and vessels, which is quite complex and requires specialized technical knowledge. Without proper segmentation, it is extremely difficult for medical staff to examine the data. This paper suggest a fully automatic method for cardiac chamber segmentation (Left Atrium and Left Ventricle pair) in 3D cardiac MRI based on artificial intelligence. The proposed method identifies the junction of Left Atrium (LA) and Left Ventricle (LV) using neural networks. The features used for this purpose are based on shape, size and position. Then it uses traditional methods to track and stack the upper and lower slices based on neighborhood. I.e. a 3D model of the segmented LA and LV is reconstructed from the 2D format. This enhanced 3D image model helps in deducing quality information for the diagnosis of various heart diseases. The proposed algorithm shows acceptable performances for all planes of LV and LA. We have achieved 91.57% mean segmentation accuracy. The proposed algorithm is not effected by the thickness of the slices. It is simple and computationally less intensive than existing algorithms.
Keywords: Cardiac MRI segmentation, left ventricle segmentation, left atrium segmentation, heart chamber segmentation, format conversion, image quality enhancement
