Evaluation of fully automated myocardial segmentation techniques in native and contrast‐enhanced T1‐mapping cardiovascular magnetic resonance images using fully convolutional neural networks
Authors: Farrag Nadia A. , Lochbihler Aidan , White James A. , Ukwatta Eranga 
Published: November 2020

Purpose

T1‐mapping cardiac magnetic resonance (CMR) imaging permits noninvasive quantification of myocardial fibrosis (MF); however, manual delineation of myocardial boundaries is time‐consuming and introduces user‐dependent variability for such measurements. In this study, we compare several automated pipelines for myocardial segmentation of the left ventricle (LV) in native and contrast‐enhanced T1‐maps using fully convolutional neural networks (CNNs).

Methods

Sixty patients with known MF across three distinct cardiomyopathy states (20 ischemic (ICM), 20 dilated (DCM), and 20 hypertrophic (HCM)) underwent a standard CMR imaging protocol inclusive of cinematic (CINE), late gadolinium enhancement (LGE), and pre/post‐contrast T1 imaging. Native and contrast‐enhanced T1‐mapping was performed using a shortened modified Look‐Locker imaging (shMOLLI) technique at the basal, mid‐level, and/or apex of the LV. Myocardial segmentations in native and post‐contrast T1‐maps were performed using three state‐of‐the‐art CNN‐based methods: standard U‐Net, densely connected neural networks (Dense Nets), and attention networks (Attention Nets) after dividing the dataset using fivefold cross validation. These direct segmentation techniques were compared to an alternative registration‐based segmentation method, wherein spatially corresponding CINE images are segmented automatically using U‐Net, and a nonrigid registration technique transforms and propagates CINE contours to the myocardial regions of T1‐maps. The methodologies were validated in 125 native and 100 contrast‐enhanced T1‐maps using standard segmentation accuracy metrics. Pearson correlation coefficientrand Bland–Altman analysis were used to compare the computed global T1 values derived by manual, U‐Net, and CINE registration methodologies.

Results

The U‐Net‐based method yielded optimal results in myocardial segmentation of native, contrast‐enhanced, and CINE images compared to Dense Nets and Attention Nets. The direct U‐Net‐based method outperformed the CINE registration‐based method in native T1‐maps, yielding Dice similarity coefficient (DSC) of 82.7 ± 12% compared to 81.4 ± 6.9% (P < 0.0001). However, in contrast‐enhanced T1‐maps, the CINE‐registration‐based method outperformed direct U‐Net segmentation, yielding DSC of 77.0 ± 9.6% vs 74.2 ± 18% across all patient groups (P = 0.0014) and specifically 73.2 ± 7.3% vs 65.5 ± 18% in the ICM patient group. High linear correlation of global T1 values was demonstrated in Pearson analysis of the U‐Net‐based technique and the CINE‐registration technique in both native T1‐maps (r = 0.93,P < 0.0001 and r = 0.87,P < 0.0001, respectively) and contrast‐enhanced T1‐maps (r = 0.93,P < 0.0001 and r = 0.98,P < 0.0001, respectively).

Conclusions

The direct U‐Net‐based myocardial segmentation technique provided accurate, fully automated segmentations in native and contrast‐enhanced T1‐maps. Myocardial borders can alternatively be segmented from spatially matched CINE images and applied to T1‐maps via deformation and propagation through a modality‐independent neighborhood descriptor (MIND). The direct U‐Net approach is more efficient in myocardial segmentation of native T1‐maps and eliminates cross‐technique dependence. However, the CINE‐registration‐based technique may be more appropriate for contrast‐enhanced T1‐maps and/or for patients with dense regions of replacement fibrosis, such as those with ICM.