  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.
