Abstract
Atrial fibrillation (AF) causes irregular heart rhythm, and its incidence and prevalence are increasing worldwide. It was estimated that 46.3 million individuals were living with AF in 2016. Late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) offers an option to image the left atrium (LA) and detect scars in the chamber, which play a central role in the treatment of AF in patients. This study proposes a deep convolutional neural network approach to automate segmentation of the LA for LGE MRI images and quantify the scars in the chamber, which are otherwise tedious and time-consuming tasks to be performed manually. The proposed method was trained and evaluated using the datasets provided by the LAScarQS 2022 challenge organizers. A total of 194 LGE MRI datasets were used in this study which were acquired from three different clinical centers. The challenge is divided into two tasks. For the first task, only the post-ablation LGE MRI scans are considered where the objective is to delineate the LA and scar. The second task considers both pre and post-ablation scans where the objective is to segment the LA. The performance of the algorithm is evaluated using Dice similarity (DM), average surface distance (ASD) and Hausdorff distance (HD) metrics. For the first task, the proposed approach yielded 90.71%, 1.681, and 21.45 for average DM, ASD and HD values for the segmentation of LA from the validation set. The corresponding values for the second task are 89.32%, 1.613, and 15.86, respectively. The proposed method yielded an average DM of 63.31% for the delineation of LA scar from the validation set.
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Acknowledgment
The authors wish to thank the challenge organizers for providing train and test datasets as well as performing the algorithm evaluation. The authors of this paper declare that the segmentation method they implemented for participation in the LAScarQS 2022 challenge has not used any pre-trained models nor additional datasets other than those provided by the organizers. This research was enabled in part by support provided by WestGrid (www.westgrid.ca) and the Digital Research Alliance of Canada (alliancecan.ca).
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Punithakumar, K., Noga, M. (2023). Automated Segmentation of the Left Atrium and Scar Using Deep Convolutional Neural Networks. In: Zhuang, X., Li, L., Wang, S., Wu, F. (eds) Left Atrial and Scar Quantification and Segmentation. LAScarQS 2022. Lecture Notes in Computer Science, vol 13586. Springer, Cham. https://doi.org/10.1007/978-3-031-31778-1_14
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