Key result
MADRU-Net outperforms state-of-the-art approaches for automated LA segmentation in LGE-MRI without postprocessing.
Why the study?
Fully automated, efficient, and generalized left atrial segmentation from cardiac MRIs is desirable due to limited available automatic techniques and challenges in apical and basal slice segmentation.
The proposed MADRU-Net deep learning architecture provides fully automated, highly accurate left atrial segmentation in cardiac MRI, outperforming existing state-of-the-art methods.
May advance research segmentation workflows; leaves open prospective clinical validation before adoption.
Treatment success for atrial fibrillation (AF) has been suboptimal until now, even though it is among the most frequent types of sustained atrial arrhythmia. Magnetic resonance imaging (MRI), a noninvasive imaging modality, can boost treatment efficacy. However, only a few automatic techniques for segmenting the atria in MRIs are available. With the recent improvements in deep learning methodologies, fully automated, efficient, and generalized left atrial segmentation from MRIs is desirable. Apical and basal slice segmentation is also a severe concern in short-axis cardiac MRI due to trabeculations present close to the apex and complex geometry of contours at the base. This article suggests a novel multiscale attention-based deep residual U-Net (MADRU-Net) to deliver fully automated left atrial segmentation in late gadolinium-enhanced (LGE)-MRI. It is an effective tool for short-axis cardiac MRI, inspired by the strength of attention gate (AG), U-Net, deep supervision, and multiple data augmentation techniques. It offers four benefits. First, AG concentrates on objects of various sizes and discovers to inhibit insignificant areas of an input MRI while recognizing significant features meaningful for segmentation. Second, the network’s extensive skip connections improve information transmission, permitting researchers to develop network infrastructure with lower complexity but better accuracy. Third, extensive data augmentation provides better training of model. Finally, deep supervision makes loss estimation easier at all feature dimensions excluding at least two, enabling gradients to be incorporated in greater depth into the network and striving to improve each layer’s training in MADRU-Net. The 2018 Atrium Segmentation Challenge (ASC) and Automated Cardiac Diagnosis Challenge (ACDC) 2017 datasets have been used to determine efficacy. The model trained on the ASC dataset was also tested on the Left Atrial and Scar Quantification and Segmentation Challenge (LAScarQS) 2022 dataset for generalization of the algorithm. ACDC dataset’s pretrained weights are used to improve model training performance on the ASC dataset. MADRU-Net surpassed almost all the approaches comparatively, demonstrating its superiority over recently invented state-of-the-art approaches without any postprocessing.
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Singh et al. (2023) studied Atrial fibrillation / Cardiac MRI segmentation. MADRU-Net (Multiscale Attention-Based Deep Residual U-Net) vs. State-of-the-art approaches was evaluated on Left atrial segmentation efficacy. MADRU-Net surpassed almost all comparative state-of-the-art approaches for fully automated left atrial segmentation in late gadolinium-enhanced MRI without any postprocessing.
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