Abstract
In this paper, we propose an approach for automatic 3D atrial segmentation from Gadolinium-enhanced MRIs based on volumetric fully convolutional networks. The entire framework consists of two networks, the first network is to roughly locate the atrial center based on a low-resolution down-sampled version of the input and cut out a fixed size area that covers the atrial cavity, leaving out other pixels irrelevant to reduce memory consumption, and the second network is to precisely segment atrial cavity from the cropped sub-regions obtained from last step. Both two networks are trained end-to-end from scratch using 2018 Atrial Segmentation Challenge (http://atriaseg2018.cardiacatlas.org/) dataset which contains 100 GE-MRIs for training, and our method achieves satisfactory segmentation accuracy, up to 0.932 in Dice Similarity Coefficient score evaluated on the 54 testing samples, which ranks 1st among all participants.
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Xia, Q., Yao, Y., Hu, Z., Hao, A. (2019). Automatic 3D Atrial Segmentation from GE-MRIs Using Volumetric Fully Convolutional Networks. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science(), vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_23
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DOI: https://doi.org/10.1007/978-3-030-12029-0_23
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