MA-SOCRATIS: An automatic pipeline for robust segmentation of the left ventricle and scar. (October 2021)

• Record Type: Journal Article
• Authors: Mamalakis, Michail; Garg, Pankaj; Nelson, Tom; Lee, Justin; Wild, Jim M.; Clayton, Richard H.
• Is Part Of: Computerized medical imaging and graphics. Volume 93(2021)
• Publication Date: 2021-10
• Subjects: 41A05 -- 41A10 -- 65D05 -- 65D17; Cardiac MRI -- Unsupervised -- Automatic segmentation -- Machine learning -- Cardiac segmentation -- Left ventricle -- Scars; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2021.101982
• Languages: English
• ISSNs: 0895-6111

Abstract

Graphical abstract: Highlights: An unsupervised automatic segmentation pipeline, which does not need training or tuning. A robust unsupervised combination of Rician–Gaussian mixture models and watershed techniques for automatic scar segmentation of LV LGE-MRI images. Evaluation of the robustness and generalization of MA-SOCRATIS; tested in two unrelated challenging LGE-MRI cohorts without any training, tuning or transfer learning. Verified the hypothesis of overestimation or underestimation of an automatic pipeline accuracy value result if the evaluation is based on biased manual segmentation GT. Abstract: Multi-atlas segmentation of cardiac regions and total infarct scar (MA-SOCRATIS) is an unsupervised automatic pipeline to segment left ventricular myocardium and scar from late gadolinium enhanced MR images (LGE-MRI) of the heart. We implement two different pipelines for myocardial and scar segmentation from short axis LGE-MRI. Myocardial segmentation has two steps; initial segmentation and re-estimation. The initial segmentation step makes a first estimate of myocardium boundaries by using multi-atlas segmentation techniques. The re-estimation step refines the myocardial segmentation by a combination of k-means clustering and a geometric median shape variation technique. An active contour technique determines the unhealthy and healthy myocardial wall. The scar segmentation pipeline is a combination of a Rician–Gaussian mixture model and full width at half maximum (FWHM) thresholding, to determine the intensity pixels in scar regions. Following this step a watershed method with an automatic seed-points framework segments the final scar region. MA-SOCRATIS was evaluated using two different datasets. In both datasets ground truths were based on manual segmentation of short axis images from LGE-MRI scans. The first dataset included 40 patients from the MS-CMRSeg 2019 challenge dataset (STACOM at MICCAI 2019). The second is a collection of 20 patients with scar regions that are challenging to segment. MA-SOCRATIS achieved robust and accurate performance in automatic segmentation of myocardium and scar regions without the need of training or tuning in both cohorts, compared with state-of-the-art techniques (intra-observer and inter observer myocardium segmentation: 81.9% and 70% average Dice value, and scar (intra-observer and inter observer segmentation: 70.5% and 70.5% average Dice value).