Abstract
In this paper, we propose a fast automatic method that segments left atrial cavity from 3D GE-MRIs without any manual assistance, using a fully convolutional network (FCN) and transfer learning. This FCN is the base network of VGG-16, pre-trained on ImageNet for natural image classification, and fine tuned with the training dataset of the MICCAI 2018 Atrial Segmentation Challenge. It relies on the “pseudo-3D” method published at ICIP 2017, which allows for segmenting objects from 2D color images which contain 3D information of MRI volumes. For each \(n^{\text {th}}\) slice of the volume to segment, we consider three images, corresponding to the \((n-1)^{\text {th}}\), \(n^{\text {th}}\), and \((n+1)^{\text {th}}\) slices of the original volume. These three gray-level 2D images are assembled to form a 2D RGB color image (one image per channel). This image is the input of the FCN to obtain a 2D segmentation of the \(n^{\text {th}}\) slice. We process all slices, then stack the results to form the 3D output segmentation. With such a technique, the segmentation of the left atrial cavity on a 3D volume takes only a few seconds. We obtain a Dice score of 0.92 both on the training set in our experiments before the challenge, and on the test set of the challenge.
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Acknowledgments
The authors want to thank the organizers of the Atrial Segmentation Challenge. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Quadro P6000 GPU used for this research.
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Puybareau, É. et al. (2019). Left Atrial Segmentation in a Few Seconds Using Fully Convolutional Network and Transfer Learning. 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_37
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DOI: https://doi.org/10.1007/978-3-030-12029-0_37
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