Segmentation of the left ventricular (LV) myocardium in 2-D essential for clinical decision making, especially in geometry measurement and index computation. However, segmenting the myocardium is a time-consuming process and challenging due to the fuzzy boundary caused by the low image quality. The ground-truth label is employed as pixel-level class associations or shape regulation in segmentation, which works limit for effective feature enhancement for 2-D echocardiography. We propose a training strategy named multiconstrained aggregate learning (referred to as MCAL), which leverages anatomical knowledge learned through ground-truth labels to infer segmented parts and discriminate boundary pixels. The new framework encourages the model to focus on the features in accordance with the learned anatomical representations, and the training objectives incorporate a boundary distance transform weight (BDTW) to enforce a higher weight value on the boundary region, which helps to improve the segmentation accuracy. The proposed method is built as an end-to-end framework with a top-down, bottom-up architecture with skip convolution fusion blocks and carried out on two datasets (our dataset and the public CAMUS dataset). The comparison study shows that the proposed network outperforms the other segmentation baseline models, indicating that our method is beneficial for boundary pixels discrimination in segmentation.
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