4 research outputs found
Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle
In this paper, we propose a graphcut method to segment the cardiac right ventricle (RV) and left ventricle (LV) by using context information from each other. Contextual information is very helpful in medical image segmentation because the relative arrangement of different organs is the same. In addition to the conventional log-likelihood penalty, we also include a "context penalty” that captures the geometric relationship between the RV and LV. Contextual information for the RV is obtained by learning its geometrical relationship with respect to the LV. Similarly, RV provides geometrical context information for LV segmentation. The smoothness cost is formulated as a function of the learned context which helps in accurate labeling of pixels. Experimental results on real patient datasets from the STACOM database show the efficacy of our method in accurately segmenting the LV and RV. We also conduct experiments on simulated datasets to investigate our method's robustness to noise and inaccurate segmentation
Multi-views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images
Left ventricular (LV) volumes estimation is a critical procedure for cardiac
disease diagnosis. The objective of this paper is to address direct LV volumes
prediction task. Methods: In this paper, we propose a direct volumes prediction
method based on the end-to-end deep convolutional neural networks (CNN). We
study the end-to-end LV volumes prediction method in items of the data
preprocessing, networks structure, and multi-views fusion strategy. The main
contributions of this paper are the following aspects. First, we propose a new
data preprocessing method on cardiac magnetic resonance (CMR). Second, we
propose a new networks structure for end-to-end LV volumes estimation. Third,
we explore the representational capacity of different slices, and propose a
fusion strategy to improve the prediction accuracy. Results: The evaluation
results show that the proposed method outperforms other state-of-the-art LV
volumes estimation methods on the open accessible benchmark datasets. The
clinical indexes derived from the predicted volumes agree well with the ground
truth (EDV: R2=0.974, RMSE=9.6ml; ESV: R2=0.976, RMSE=7.1ml; EF: R2=0.828, RMSE
=4.71%). Conclusion: Experimental results prove that the proposed method may be
useful for LV volumes prediction task. Significance: The proposed method not
only has application potential for cardiac diseases screening for large-scale
CMR data, but also can be extended to other medical image research fieldsComment: to appear on Transactions on Biomedical Engineerin