Key result
The AtrialJSQnet deep learning framework achieved a Dice score of 0.913 for left atrial segmentation and 0.543 for scar quantification, demonstrating improved performance over separate task models.
Why the study?
Automatic segmentation of the left atrium and atrial scar from LGE MRI remains challenging, and previous methods addressed these tasks independently without incorporating their intrinsic spatial relationship.
AtrialJSQnet enables simultaneous, end-to-end left atrial segmentation and scar quantification from LGE MRI with competitive accuracy, which may assist in guiding ablation therapy for atrial fibrillation.
Left atrial (LA) and atrial scar segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is an important task in clinical practice. %, to guide ablation therapy and predict treatment results for atrial fibrillation (AF) patients. The automatic segmentation is however still challenging, due to the poor image quality, the various LA shapes, the thin wall, and the surrounding enhanced regions. Previous methods normally solved the two tasks independently and ignored the intrinsic spatial relationship between LA and scars. In this work, we develop a new framework, namely AtrialJSQnet, where LA segmentation, scar projection onto the LA surface, and scar quantification are performed simultaneously in an end-to-end style. We propose a mechanism of shape attention (SA) via an explicit surface projection, to utilize the inherent correlation between LA and LA scars. In specific, the SA scheme is embedded into a multi-task architecture to perform joint LA segmentation and scar quantification. Besides, a spatial encoding (SE) loss is introduced to incorporate continuous spatial information of the target, in order to reduce noisy patches in the predicted segmentation. We evaluated the proposed framework on 60 LGE MRIs from the MICCAI2018 LA challenge. Extensive experiments on a public dataset demonstrated the effect of the proposed AtrialJSQnet, which achieved competitive performance over the state-of-the-art. The relatedness between LA segmentation and scar quantification was explicitly explored and has shown significant performance improvements for both tasks. The code and results will be released publicly once the manuscript is accepted for publication via https://zmiclab.github.io/projects.html.
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Li et al. (2020) studied Atrial Fibrillation (Left Atrial and Scar Segmentation) (n=130). AtrialJSQnet (Deep Learning Framework) vs. Separate U-Net models and other baseline methods was evaluated on Dice volume overlap for Left Atrial segmentation (DiceLA). The AtrialJSQnet deep learning framework achieved a Dice score of 0.913 for left atrial segmentation and 0.543 for scar quantification, demonstrating improved performance over separate task models.
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