Automatic left ventricle volume and mass quantification from 2D cine-MRI: Investigating papillary muscle influence

• Published in: Medical engineering & physics 2024-05, Vol.127, p.104162, Article 104162
• Main Authors: BACCOUCH, Wafa, OUESLATI, Sameh, SOLAIMAN, Basel, LAHIDHEB, Dhaker, LABIDI, Salam
• Format: Article
• Language: English
• Subjects: Automatic quantification; Automatic segmentation; Automation; Cardiac magnetic resonance; Female; Heart Ventricles - diagnostic imaging; Humans; Image Processing, Computer-Assisted - methods; Left ventricle mass; Left ventricle volume; Magnetic Resonance Imaging, Cine - methods; Male; Middle Aged; Neural Networks, Computer; Organ Size; Papillary muscles; Papillary Muscles - diagnostic imaging; Papillary Muscles - physiology; Stroke Volume
• ISSN: 1350-4533; 1873-4030; 1873-4030
• DOI: 10.1016/j.medengphy.2024.104162

•Quantification methods based on segmentation is more accurate than direct methods.•Fully automatic quantification of left ventricle volume and mass from 2D cine-MRI.•Inclusion of papillary muscles affects segmentation and quantification performance.•Detection and quantification of the heart contraction abnormalities. Early detection of cardiovascular diseases is based on accurate quantification of the left ventricle (LV) function parameters. In this paper, we propose a fully automatic framework for LV volume and mass quantification from 2D-cine MR images already segmented using U-Net. The general framework consists of three main steps: Data preparation including automatic LV localization using a convolution neural network (CNN) and application of morphological operations to exclude papillary muscles from the LV cavity. The second step consists in automatically extracting the LV contours using U-Net architecture. Finally, by integrating temporal information which is manifested by a spatial motion of myocytes as a third dimension, we calculated LV volume, LV ejection fraction (LVEF) and left ventricle mass (LVM). Based on these parameters, we detected and quantified cardiac contraction abnormalities using Python software. CNN was trained with 35 patients and tested on 15 patients from the ACDC database with an accuracy of 99,15 %. U-Net architecture was trained using ACDC database and evaluated using local dataset with a Dice similarity coefficient (DSC) of 99,78 % and a Hausdorff Distance (HD) of 4.468 mm (p < 0,001). Quantification results showed a strong correlation with physiological measures with a Pearson correlation coefficient (PCC) of 0,991 for LV volume, 0.962 for LVEF, 0.98 for stroke volume (SV) and 0.923 for LVM after pillars’ elimination. Clinically, our method allows regional and accurate identification of pathological myocardial segments and can serve as a diagnostic aid tool of cardiac contraction abnormalities. Experimental results prove the usefulness of the proposed method for LV volume and function quantification and verify its potential clinical applicability.