  Segmentation of the Left ventricle (LV) is a crucial step for quantitative
measurements such as area, volume, and ejection fraction. However, the
automatic LV segmentation in 2D echocardiographic images is a challenging task
due to ill-defined borders, and operator dependence issues (insufficient
reproducibility). U-net, which is a well-known architecture in medical image
segmentation, addressed this problem through an encoder-decoder path. Despite
outstanding overall performance, U-net ignores the contribution of all semantic
strengths in the segmentation procedure. In the present study, we have proposed
a novel architecture to tackle this drawback. Feature maps in all levels of the
decoder path of U-net are concatenated, their depths are equalized, and
up-sampled to a fixed dimension. This stack of feature maps would be the input
of the semantic segmentation layer. The proposed network yielded
state-of-the-art results when comparing with results from U-net, dilated U-net,
and deeplabv3, using the same dataset. An average Dice Metric (DM) of 0.945,
Hausdorff Distance (HD) of 1.62, Jaccard Coefficient (JC) of 0.97, and Mean
Absolute Distance (MAD) of 1.32 are achieved. The correlation graph,
bland-altman analysis, and box plot showed a great agreement between automatic
and manually calculated volume, area, and length.
