Left Atrial Segmentation in a Few Seconds Using Fully Convolutional Network and Transfer Learning
Résumé
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 th slice of the volume to segment, we consider three images, corresponding to the (n − 1) th , n th , and (n + 1) 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 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 reached a dice of 0.911 on the training set.
Domaines
Traitement des images [eess.IV]Origine | Fichiers produits par l'(les) auteur(s) |
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