Semi-automated Image Segmentation of the Midsystolic Left Ventricular Mitral Valve Complex in Ischemic Mitral Regurgitation.
Ahmed H AlyAbdullah H AlyMahmoud ElrakhawyKirlos HarounLuis Prieto-RiascosRobert C GormanNatalie YushkevichYoshiaki SaitoJoseph H GormanRobert C GormanPaul A YushkevichAlison M PouchPublished in: Statistical atlases and computational models of the heart. STACOM (Workshop) (2019)
Ischemic mitral regurgitation (IMR) is primarily a left ventricular disease in which the mitral valve is dysfunctional due to ventricular remodeling after myocardial infarction. Current automated methods have focused on analyzing the mitral valve and left ventricle independently. While these methods have allowed for valuable insights into mechanisms of IMR, they do not fully integrate pathological features of the left ventricle and mitral valve. Thus, there is an unmet need to develop an automated segmentation algorithm for the left ventricular mitral valve complex, in order to allow for a more comprehensive study of this disease. The objective of this study is to generate and evaluate segmentations of the left ventricular mitral valve complex in pre-operative 3D transesophageal echocardiography using multi-atlas label fusion. These patient-specific segmentations could enable future statistical shape analysis for clinical outcome prediction and surgical risk stratification. In this study, we demonstrate a preliminary segmentation pipeline that achieves an average Dice coefficient of 0.78 ± 0.06.
Keyphrases
- mitral valve
- left ventricular
- left atrial
- deep learning
- hypertrophic cardiomyopathy
- cardiac resynchronization therapy
- acute myocardial infarction
- heart failure
- aortic stenosis
- convolutional neural network
- machine learning
- computed tomography
- magnetic resonance
- pulmonary arterial hypertension
- ischemia reperfusion injury
- pulmonary artery
- ejection fraction