# Left Atrial Segmentation In a Few Seconds Using Fully Convolutional Network and Transfer Learning

## 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 ${\displaystyle n^{\text{th}}}$ slice of the volume to segment, we consider three imagescorresponding to the ${\displaystyle (n-1)^{\text{th}}}$, ${\displaystyle n^{\text{th}}}$and ${\displaystyle (n+1)^{\text{th}}}$ 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 ${\displaystyle n^{\text{th}}}$ 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.

## Bibtex (lrde.bib)

@InProceedings{	  puybareau.18.stacom,
author	= {\'Elodie Puybareau and Zhou Zhao and Younes Khoudli and
Edwin Carlinet and Yongchao Xu and J\'er\^ome Lacotte and
Thierry G\'eraud},
title		= {Left Atrial Segmentation In a Few Seconds Using Fully
Convolutional Network and Transfer Learning},
booktitle	= {Proceedings of the Workshop on Statistical Atlases and
Computational Modelling of the Heart (STACOM 2018), in
conjunction with MICCAI},
year		= 2019,
series	= {Lecture Notes in Computer Science},
publisher	= {Springer},
volume	= {11395},
pages		= {339--347},
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.}
}