Fully Automatic 3D Bi-Atria Segmentation from Late Gadolinium-Enhanced MRIs Using Double Convolutional Neural Networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

External Institution(s)

  • Auckland Bioengineering Institute, University of Auckland
  • The University of Auckland

Details

Original languageEnglish (US)
Title of host publicationStatistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers
EditorsMihaela Pop, Maxime Sermesant, Oscar Camara, Xiahai Zhuang, Shuo Li, Alistair Young, Tommaso Mansi, Avan Suinesiaputra
StatusPublished - Jan 1 2020
Event10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 13 2019Oct 13 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12009 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/13/201910/13/2019

Abstract

Segmentation of the 3D human atria from late gadolinium-enhanced (LGE)-MRIs is crucial for understanding and analyzing the underlying atrial structures that sustain atrial fibrillation (AF), the most common cardiac arrhythmia. However, due to the lack of a large labeled dataset, current automated methods have only been developed for left atrium (LA) segmentation. Since AF is sustained across both the LA and right atrium (RA), an automatic bi-atria segmentation method is of high interest. We have therefore created a 3D LGE-MRI database from AF patients with both LA and RA labels to train a double, sequentially used convolutional neural network (CNN) for automatic LA and RA epicardium and endocardium segmentation. To mitigate issues regarding the severe class imbalance and the complex geometry of the atria, the first CNN accurately detects the region of interest (ROI) containing the atria and the second CNN performs targeted regional segmentation of the ROI. The CNN comprises of a U-Net backbone enhanced with residual blocks, pre-activation normalization, and a Dice loss to improve accuracy and convergence. The receptive field of the CNN was increased by using 5 × 5 kernels to capture large variations in the atrial geometry. Our algorithm segments and reconstructs the LA and RA within 2 s, achieving a Dice accuracy of 94% and a surface-to-surface distance error of approximately 1 pixel. To our knowledge, the proposed approach is the first of its kind, and is currently the most robust automatic bi-atria segmentation method, creating a solid benchmark for future studies.

    Research areas

  • Atrial segmentation, Convolutional neural network, MRI

Citation formats

APA

Xiong, Z., Nalar, A., Jamart, K., Stiles, M. K., Fedorov, V. V., & Zhao, J. (2020). Fully Automatic 3D Bi-Atria Segmentation from Late Gadolinium-Enhanced MRIs Using Double Convolutional Neural Networks. In M. Pop, M. Sermesant, O. Camara, X. Zhuang, S. Li, A. Young, T. Mansi, ... A. Suinesiaputra (Eds.), Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers (pp. 63-71). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12009 LNCS). Springer. https://doi.org/10.1007/978-3-030-39074-7_7

Harvard

Xiong, Z, Nalar, A, Jamart, K, Stiles, MK, Fedorov, VV & Zhao, J 2020, Fully Automatic 3D Bi-Atria Segmentation from Late Gadolinium-Enhanced MRIs Using Double Convolutional Neural Networks. in M Pop, M Sermesant, O Camara, X Zhuang, S Li, A Young, T Mansi & A Suinesiaputra (eds), Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12009 LNCS, Springer, pp. 63-71, 10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019, Shenzhen, China, 10/13/2019. https://doi.org/10.1007/978-3-030-39074-7_7