Abstract
Understanding the anatomical structure of left atrial (LA) is crucial for clinical treatment of atrial fibrillation (AF). Gadolinium Enhanced Magnetic Resonance Imaging (GE-MRI) provides clarity images of LA structure. However, the most of LA structure analysis on GE-MRI studies are based on subjective manual segmentation. An efficient and objective segmentation method in GE-MRI is highly demanded. Although deep learning based method has achieved great success on some medical image segmentations, solving LA segmentation through deep learning is still an unsatisfied field. In this paper, we handle this unmet clinical need by exploring two convolutional neural networks (CNNs) structures, fully convolutional network (FCN) and U-Net, to improve the accuracy and efficiency of LA segmentation. Both models were trained and evaluated on GE-MRI dataset provided by 2018 atrial segmentation challenge. The results show that FCN-based LA automatic segmentation method achieves Dice score over 82%; U-Net method achieves Dice score over 83%.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China, grant no. 61571165. Thanks to Jichao Zhao and Zhaohan Xiong who organized the 2018 atrial segmentation challenge.
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Liu, Y., Dai, Y., Yan, C., Wang, K. (2019). Deep Learning Based Method for Left Atrial Segmentation in GE-MRI. 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_34
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DOI: https://doi.org/10.1007/978-3-030-12029-0_34
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