@inproceedings{ca980f17ea0d47b890407c7d08981582,
title = "Deep Learning Based Method for Left Atrial Segmentation in GE-MRI",
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\%.",
keywords = "Deep learning, GE-MRI, Left atrial segmentation",
author = "Yashu Liu and Yangyang Dai and Cong Yan and Kuanquan Wang",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 ; Conference date: 16-09-2018 Through 16-09-2018",
year = "2019",
doi = "10.1007/978-3-030-12029-0\_34",
language = "英语",
isbn = "9783030120283",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "311--318",
editor = "Alistair Young and Kawal Rhode and Tommaso Mansi and Maxime Sermesant and Shuo Li and Kristin McLeod and Mihaela Pop and Jichao Zhao",
booktitle = "Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges - 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers",
address = "德国",
}