Abstract
Automatic segmentation of the left atrium (LA) is a fundamental task for atrial fibrillation diagnosis and computer-aided ablation operation support systems. This paper presents an approach to automatically segmenting left atrium in 3D CT volumes using fully convolutional neural networks (FCNs). We train FCN for automatic segmentation of the left atrium, and then refine the segmentation results of the FCN using the knowledge of the left ventricle segmented using ASM based method. The proposed FCN models were trained on the STACOM’13 CT dataset. The results show that FCN-based left atrium segmentation achieves Dice coefficient scores over 93% with computation time below 35s per volume, despite of the high variation of LA.
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This work is supported by the National Natural Science Foundation of China under Grants 61622207, 61373074, 61225008, and 61572271.
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Liu, H., Feng, J., Feng, Z., Lu, J., Zhou, J. (2017). Left Atrium Segmentation in CT Volumes with Fully Convolutional Networks. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_5
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DOI: https://doi.org/10.1007/978-3-319-67558-9_5
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