A joint multi-scale convolutional network for fully automatic segmentation of the left ventricle

Left ventricle (LV) segmentation is crucial for quantitative analysis of the cardiac contractile function. In this paper, we propose a joint multi-scale convolutional neural network to fully automatically segment the LV. Our method adopts two kinds of multi-scale features of cardiac magnetic resonan...

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Bibliographic Details
Main Authors: Tong, Qianqian, Yuan, Zhiyong, Liao, Xiangyun, Zheng, Mianlun, Zhu, Weixu, Zhang, Guian, Ning, Munan
Format: Conference Proceeding
Language:English
Subjects:
Avalanche photodiodes
convolutional neural network
Convolutional neural networks
Feature extraction
Image segmentation
LV segmentation
Manuals
multi-scale representation
Training
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Summary:Left ventricle (LV) segmentation is crucial for quantitative analysis of the cardiac contractile function. In this paper, we propose a joint multi-scale convolutional neural network to fully automatically segment the LV. Our method adopts two kinds of multi-scale features of cardiac magnetic resonance (CMR) images, including multi-scale features directly extracted from CMR images with different scales and multi-scale features constructed by intermediate layers of standard CNN architecture. We take advantage of these two strategies and fuse their prediction results to produce more accurate segmentation results. Qualitative results demonstrate the effectiveness and robustness of our method, and quantitative evaluation indicates our method achieves LV segmentation with higher accuracy than state-of-the-art approaches.
ISSN:2381-8549
DOI:10.1109/ICIP.2017.8296855