Automatic and optimal segmentation of the left ventricle in cardiac magnetic resonance images independent of the training sets

Automatic and optimal segmentation of the left ventricle in cardiac magnetic resonance images independent of the training sets

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In cardiac imaging, the boundary of the left ventricle (LV) could be used to measure the dyssynchrony of the heart. Hence, automatic and optimal segmentation of the LV is important. Although deep learning-based methods have achieved significant break-throughs in the accuracy of segmenting LV, it relies on a great number of training sets and the reproduction quality of the tested cases. Due to the variety of patients, it is difficult or impossible to collect the complete training sets that cover all patients with different genders, races, and ages. Therefore, methods independent of the training sets are more reliable and efficient for clinical applications. In this study, the authors propose a training sets-independent method to segment LV optimally and it outperforms all available state-of-the-art training-sets-independent image segmentation methods. In addition, they propose a framework to identify the boundary of the LV automatically. They tested these segmentation methods with both good quality and poor quality images in the proposed framework and verified that the proposed segmentation method yields the optimal solution compared to other state-of-the-art training-sets-independent segmentation methods. Based on their previous research work, the identified boundaries by the proposed approach are accurate enough for calculating the dyssynchrony of the LV.


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