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A data augmentation approach to train fully convolutional networks for left ventricle segmentation.

Abstract：

:Left ventricle (LV) segmentation plays an important role in the diagnosis of cardiovascular diseases. The cardiac contractile function can be quantified by measuring the segmentation results of LVs. Fully convolutional networks (FCNs) have been proven to be able to segment images. However, a large number of annotated images are required to train the network to avoid overfitting, which is a challenge for LV segmentation owing to the limited small number of available training samples. In this paper, we analyze the influence of augmenting training samples used in an FCN for LV segmentation, and propose a data augmentation approach based on shape models to train the FCN from a few samples. We show that the balanced training samples affect the performance of FCNs greatly. Experiments on four public datasets demonstrate that the FCN trained by our augmented data outperforms most existing automated segmentation methods with respect to several commonly used evaluation measures.

journal_name

Magn Reson Imaging

journal_title

Magnetic resonance imaging

authors

Lin A,Wu J,Yang X

doi

10.1016/j.mri.2019.08.004

subject

Has Abstract

pub_date

2020-02-01 00:00:00

pages

152-164

eissn

0730-725X

issn

1873-5894

pii

S0730-725X(19)30052-9

journal_volume

66

pub_type

杂志文章

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