Myocardium Segmentation based on combining Fully Convolutional Network and Graph cut

Authors: Trần Thị Thảo*, Phạm Văn Trường*


Myocardium segmentation from cardiac MRI images is an important task in clinical diagnosis of the left ventricle (LV) function. In this paper, we proposed a new approach for myocardium segmentation based on deep neural network and Graph cut approach. The proposed method is a framework including two steps: in the first step, the fully convolutional network (FCN) was performed to obtain coarse segmentation of LV from input cardiac MR images. In the second step, Graph cut method was employed to further optimize the coarse segmentation results in order to get fine segmentation of LV. The proposed model was validated in 45 subjects of Sunnybrook database using the Dice coefficient metric and compared with other state-of-the-art approaches. Experimental results show the robustness and feasibility of the proposed method.


Myocardium Segmentation, Graph cut, Fully Convolutional Network, Deep Learning, Cardiac MRI Segmentation.
Pages : 18-23

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