Title
Two-Stage 2d Cnn For Automatic Atrial Segmentation From Lge-Mris
Abstract
Atrial fibrillation (AF) is the most common sustained heart rhythm disturbance and a leading cause of hospitalization, heart failure and stroke. In the current medical practice, atrial segmentation from medical images for clinical diagnosis and treatment, is a labor-intensive and error-prone manual process. The atrial segmentation challenge held in conjunction with the 2018 the Medical Image Computing and Computer Assisted Intervention Society (MICCAI) conference and Statistical Atlases and Computational Modelling of the Heart (STACOM), offered the opportunity to develop reliable approaches to automatically annotate and perform segmentation of the left atrial (LA) chamber using the largest available 3D late gadolinium-enhanced MRI (LGE-MRI) dataset with 154 3D LGE-MRIs and labels. For this challenge, 11 out the 27 contestants achieved more than 90% Dice score accuracy, however, a critical question remains as which is the optimal approach for LA segmentation. In this paper, we propose a two-stage 2D fully convolutional neural network with extensive data augmentation and achieves a superior segmentation accuracy with a Dice score of 93.7% using the same dataset and conditions as for the atrial segmentation challenge. Thus, our approach outperforms the methods proposed in the atrial segmentation challenge while employing less computational resources than the challenge winning method.
Year
DOI
Venue
2019
10.1007/978-3-030-39074-7_9
STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART: MULTI-SEQUENCE CMR SEGMENTATION, CRT-EPIGGY AND LV FULL QUANTIFICATION CHALLENGES
Keywords
Field
DocType
Automatic cardiac segmentation, LGE-MRI, Atrial fibrillation
Pattern recognition,Computer science,Segmentation,Artificial intelligence
Conference
Volume
ISSN
Citations 
12009
0302-9743
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Kevin Jamart100.34
Zhaohan Xiong2163.15
Gonzalo D. Maso Talou300.34
Martin K. Stiles472.12
Jichao Zhao592.05