4 research outputs found
Multi-atlas propagation based left atrium segmentation coupled with super-voxel based pulmonary veins delineation in late gadolinium-enhanced cardiac MRI
Late Gadolinium-Enhanced Cardiac MRI (LGE CMRI) is a non-invasive technique, which has shown promise in detecting native and post-ablation atrial scarring. To visualize the scarring, a precise segmentation of the left atrium (LA) and pulmonary veins (PVs) anatomy is performed as a first step—usually from an ECG gated CMRI roadmap acquisition—and the enhanced scar regions from the LGE CMRI images are superimposed. The anatomy of the LA and PVs in particular is highly variable and manual segmentation is labor intensive and highly subjective. In this paper, we developed a multi-atlas propagation based whole heart segmentation (WHS) to delineate the LA and PVs from ECG gated CMRI roadmap scans. While this captures the anatomy of the atrium well, the PVs anatomy is less easily visualized. The process is therefore augmented by semi-automated manual strokes for PVs identification in the registered LGE CMRI data. This allows us to extract more accurate anatomy than the fully automated WHS. Both qualitative visualization and quantitative assessment with respect to manual segmented ground truth showed that our method is efficient and effective with an overall mean Dice score of 0.91
Multiview two-task recursive attention model for left atrium and atrial scars segmentation
Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success. Visualisation and quantification of scar tissues require a segmentation of both the left atrium (LA) and the high intensity scar regions from LGE-CMRI images. These two segmentation tasks are challenging due to the cancelling of healthy tissue signal, low signal-to-noise ratio and often limited image quality in these patients. Most approaches require manual supervision and/or a second bright-blood MRI acquisition for anatomical segmentation. Segmenting both the LA anatomy and the scar tissues automatically from a single LGE-CMRI acquisition is highly in demand. In this study, we proposed a novel fully automated multiview two-task (MVTT) recursive attention model working directly on LGE-CMRI images that combines a sequential learning and a dilated residual learning to segment the LA (including attached pulmonary veins) and delineate the atrial scars simultaneously via an innovative attention model. Compared to other state-of-the-art methods, the proposed MVTT achieves compelling improvement, enabling to generate a patient-specific anatomical and atrial scar assessment model
Multiview Sequential Learning and Dilated Residual Learning for a Fully Automatic Delineation of the Left Atrium and Pulmonary Veins from Late Gadolinium-Enhanced Cardiac MRI Images
Accurate delineation of heart substructures is a
prerequisite for abnormality detection, for making quantitative
and functional measurements, and for computer-aided
diagnosis and treatment planning. Late Gadolinium-Enhanced
Cardiac MRI (LGE-CMRI) is an emerging imaging technology
for myocardial infarction or scar detection based on the
differences in the volume of residual gadolinium distribution
between scar and healthy tissues. While LGE-CMRI is a
well-established non-invasive tool for detecting myocardial scar
tissues in the ventricles, its application to left atrium (LA)
imaging is more challenging due to its very thin wall of the LA
and poor quality images, which may be produced because of
motion artefacts and low signal-to-noise ratio. As the
LGE-CMRI scan is designed to highlight scar tissues by altering
the gadolinium kinetics, the anatomy among different heart
substructures has less distinguishable boundaries. An accurate,
robust and reproducible method for LA segmentation is highly
in demand because it can not only provide valuable information
of the heart function but also be helpful for the further
delineation of scar tissue and measuring the scar percentage. In
this study, we proposed a novel deep learning framework
working on LGE-CMRI images directly by combining
sequential learning and dilated residual learning to delineate
LA and pulmonary veins fully automatically. The achieved
results showed accurate segmentation results compared to the
state-of-the-art methods. The proposed framework leads to an
automatic generation of a patient-specific model that can
potentially enable an objective atrial scarring assessment for the
atrial fibrillation patient