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Segmentation of the Left ventricle (LV) is a crucial step for quantitativemeasurements such as area, volume, and ejection fraction. However, theautomatic LV segmentation in 2D echocardiographic images is a challenging taskdue to ill-defined borders, and operator dependence issues (insufficientreproducibility). U-net, which is a well-known architecture in medical imagesegmentation, addressed this problem through an encoder-decoder path. Despiteoutstanding overall performance, U-net ignores the contribution of all semanticstrengths in the segmentation procedure. In the present study, we have proposeda novel architecture to tackle this drawback. Feature maps in all levels of thedecoder path of U-net are concatenated, their depths are equalized, andup-sampled to a fixed dimension. This stack of feature maps would be the inputof the semantic segmentation layer. The proposed network yieldedstate-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 MeanAbsolute Distance (MAD) of 1.32 are achieved. The correlation graph,bland-altman analysis, and box plot showed a great agreement between automaticand manually calculated volume, area, and length.

A Novel Deep Learning Based Approach for Left Ventricle Segmentation in Echocardiography: MFP-Unet