Convolutional neural network‐based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images

Abstract

Purpose: Accurate three-dimensional (3D) segmentation of myocardial replacement fibrosis (i.e., scar) is emerging as a potentially valuable tool for risk stratification and procedural planning in patients with ischemic cardiomyopathy. The main purpose of this study was to develop a semiautomated method using a 3D convolutional neural network (CNN)-based for the segmentation of left ventricle (LV) myocardial scar from 3D late gadolinium enhancement magnetic resonance (LGE-MR) images. Methods: Our proposed CNN is built upon several convolutional and pooling layers aimed at choosing appropriate features from LGE-MR images to distinguish between myocardial scar and healthy tissues of the left ventricle. In contrast to previous methods that consider image intensity as the sole feature, CNN-based algorithms have the potential to improve the accuracy of scar segmentation through the creation of unconventional features that separate scar from normal myocardium in the feature space. The first step of our pipeline was to manually delineate the left ventricular myocardium, which was used as the region of interest for scar segmentation. Our developed algorithm was trained using 265,220 volume patches extracted from ten 3D LGE-MR images, then was validated on 450,454 patches from a testing dataset of 24 3D LGE-MR images, all obtained from patients with chronic myocardial infarction. We evaluated our method in the context of several alternative methods by comparing algorithm-generated segmentations to manual delineations performed by experts. Results: Our CNN-based method reported an average Dice similarity coefficient (DSC) and Jaccard Index (JI) of 93.63% AE 2.6% and 88.13% AE 4.70%. In comparison to several previous methods, including K-nearest neighbor (KNN), hierarchical max flow (HMF), full width at half maximum (FWHM), and signal threshold to reference mean (STRM), the developed algorithm reported significantly higher accuracy for DSC with a P-value less than 0.0001. Conclusions: Our experimental results demonstrated that our CNN-based proposed method yielded the highest accuracy of all contemporary LV myocardial scar segmentation methodologies, inclusive of the most widely used signal intensity-based methods, such as FWHM and STRM. To our knowledge, this is the first description of LV myocardial scar tissue segmentation from 3D LGE-MR images using a CNN-based method.

Automated left ventricular myocardium segmentation using 3D deeply supervised attention U‐net for coronary computed tomography angiography; CT myocardium segmentation

“…This study aims to develop an automatic segmentation method to delineate the LVM volume from CCTA. In recent years, machine learning methods have been integrated into the segmentation process . Oktay et al .…”

The International Radiomics Platform – An Initiative of the German and Austrian Radiological Societies – First Application Examples

“…This also seems to be proven by current publications on the topic. Therefore, in a study published in 2019, analysis using deep neural learning has the lowest variance of less than 10 % compared to the "ground truth" in Bland-Altman plots, while the variance in the case of threshold-based methods was over 20 % [31]. A current publication in Radiology also shows that texture analyses on the basis of already preprocessed data, e. g. with T1 and T2 mapping, allows significantly better classification of patients with myocarditis [32].…”

“…Dies scheinen auch aktuelle Publikationen zu dem Thema zu belegen. So hatte in einer im Jahr 2019 publizierten Studie die Analyse unter Verwendung von "deep neural learning" in Bland-Altman-Plots gegenüber der "Ground Truth" die geringste Varianz von weniger als 10 %, während die Varianz bei schwellenwertbasierten Verfahren bei über 20 % lag [31]. Eine aktuelle Publikation in Radiology belegt außerdem, dass Texturanalysen auf Basis bereits vorprozessierter Daten, beispielsweise mit T1-und T2-Mapping, eine deutlich verbesserte Klassifikation von Patienten mit Myokarditis ermöglichen können [32].…”

Fully automated segmentation of left ventricular scar from 3D late gadolinium enhancement magnetic resonance imaging using a cascaded multi‐planar U‐Net (CMPU‐Net)

“…Some LV scar segmentation methods require LV myocardial segmentation. This is achieved through manual segmentation, through CINE segmentation and subsequent propagation of the segmentations through registration and multi‐modality label fusion strategy . As the primary goal of automated medical image analysis is to achieve both speed and user independence, those approaches require manual segmentation of LV remain suboptimal.…”