Abstract
Accurate segmentation of the shape of the left atrium (LA) is important for treatment of atrial fibrillation (AF) by catheter ablation. Interventional 3D rotational angiography (3DRA) can be used to obtain 3D images during the intervention. Low dose 3DRA poses segmentation challenges due to high image noise. There is a significant amount of research focusing on the automatic segmentation from 3DRA images, all based on an active shape or atlas-based approaches.
We present an algorithm based on a 3D deep convolutional neural network (CNN) for automated segmentation of 3DRA images to predict the shape of the LA. The CNN is based on the U-Net architecture and consists of an encoder and a decoder part. It is designed to be trained end-to-end from scratch on interactive semi-automated 3DRA images, which include the body of the LA and the proximal pulmonary veins up to the first branching vessel.
The CNN is trained and validated using 5-fold cross-validation on 20 3DRA images by computing the Dice score (0.959 ± 0.015), recall (0.962 ± 0.026), precision (0.957 ± 0.021) and mean surface distance (0.716 ± 0.276 mm). We further validated the algorithm on an additional data set of 5 images. The algorithm achieved a Dice score and mean surface distance of 0.937 ± 0.016 and 1.500 ± 0.368 respectively.
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Bamps, K. et al. (2020). DeepLA: Automated Segmentation of Left Atrium from Interventional 3D Rotational Angiography Using CNN. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges. STACOM 2019. Lecture Notes in Computer Science(), vol 12009. Springer, Cham. https://doi.org/10.1007/978-3-030-39074-7_15
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DOI: https://doi.org/10.1007/978-3-030-39074-7_15
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