This paper introduces a left atrial segmentation pipeline that utilises a deep neural network for learning segmentations of the LA from Gadolinium enhancement magnetic resonance images (GE-MRI). The trainable fully-convolutional neural network consists of an encoder network and a corresponding decoder network followed by a pixel-wise classification layer. The entire network has 17 convolutional layers, with the encoder network containing 5 convolutional layers, and the decoder network containing 11 convolution layers with 1 additional convolution layer in between. The training image database consisted of manually annotated GE-MRI images ((Formula Presented)
CITATION STYLE
de Vente, C., Veta, M., Razeghi, O., Niederer, S., Pluim, J., Rhode, K., & Karim, R. (2019). Convolutional Neural Networks for Segmentation of the Left Atrium from Gadolinium-Enhancement MRI Images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11395 LNCS, pp. 348–356). Springer Verlag. https://doi.org/10.1007/978-3-030-12029-0_38
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