## 6 Conclusions  
This paper introduced an efficient encoder-decoder-based architecture for whole heart segmentation on CT and MRI images. Accurate heart and its substructures segmentation enable faster visualization of target structures and data navigation, which benefits clinical practice by reducing diagnosis and prognosis times. Our proposed method introduces a novel connectivity structure of residual unit that we refer to as feature merge residual unit (FM-Pre-ResNet). The proposed connectivity allows the creation of distinctly deep models without an increase in the number of parameters compared to the Pre-ResNet units. Furthermore, we construct an encoder-decoder-based architecture that incorporates the VAE encoder at the segmentation encoder output to have a regularizing effect on the \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Authors** & **Method** & **DSC** & **JI** & **SD (mm)** & **HD (mm)** \\ \hline [PERSON] et al. [12] & Multi Atlas + CNN & \(0.838\pm 0.152\) & \(0.742\pm 0.161\) & \(4.812\pm 13.604\) & \(34.634\pm 12.351\) \\ [PERSON] et al. [14] & Localization + & \(0.908\pm 0.086\) & \(0.832\pm 0.037\) & \(1.117\pm 0.250\) & \(25.242\pm 10.813\) \\  & segmentaiton CNN & & & & \\ [PERSON] et al. [19] & multi planar CNN & \(0.879\pm 0.079\) & \(0.792\pm 0.106\) & \(1.538\pm 1.006\) & \(28.481\pm 11.434\) \\ [PERSON] et al. [16] & Statistical shape & \(0.894\pm 0.030\) & \(0.810\pm 0.048\) & \(1.387\pm 0.516\) & \(31.146\pm 13.203\) \\  & priors + CNN & & & & \\ [PERSON] et al. [23] & Deeply supervised & \(0.849\pm 0.061\) & \(0.742\pm 0.086\) & \(1.925\pm 0.924\) & \(44.880\pm 16.084\) \\  & 3D U-Net & & & & \\ [PERSON] et al. [21] & multi planar 2D CNN & \(\mathbf{0.914\pm 0.075}\) & \(\mathbf{0.840\pm 0.075}\) & \(1.42\pm 0.46\) & \(28.042\pm 12.142\) \\
**Proposed method** & **FM-Pre-ResNet + VAE** & \(0.9039\pm 0.0517\) & \(0.8224\pm 0.0571\) & \(\mathbf{1.1093\pm 0.0215}\) & \(\mathbf{15.362\pm 12.3737}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of an average DSC, JI, SD, and HD of the state-of-the-art whole heart segmentation methods on CT images. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Authors** & **Method** & **DSC** & **JI** & **SD (mm)** & **HD (mm)** \\ \hline [PERSON] et al. [12] & Multi Atlas + CNN & \(0.817\pm 0.059\) & \(0.695\pm 0.081\) & \(2.420\pm 0.925\) & \(30.938\pm 12.190\) \\ [PERSON] et al. [14] & Localization + & \(0.863\pm 0.043\) & \(0.762\pm 0.064\) & \(1.890\pm 0.781\) & \(30.227\pm 14.046\) \\  & segmentaiton CNN & & & & \\ [PERSON] et al. [19] & multi planar CNN & \(0.818\pm 0.096\) & \(0.701\pm 0.118\) & \(3.040\pm 3.097\) & \(40.092\pm 21.119\) \\ [PERSON] et al. [16] & Statistical shape & \(0.855\pm 0.069\) & \(0.753\pm 0.094\) & \(1.963\pm 1.012\) & \(30.201\pm 13.2216\) \\  & priors + CNN & & & & \\ [PERSON] et al. [23] & Deeply supervised & \(0.674\pm 0.182\) & \(0.532\pm 0.178\) & \(9.776\pm 0.924\) & \(44.880\pm 16.084\) \\  & 3D U-Net & & & & \\ [PERSON] et al. [21] & multi planar 2D CNN & \(0.89\pm 0.075\) & \(0.840\pm 0.075\) & \(\mathbf{1.42\pm 0.46}\) & \(28.042\pm 12.142\) \\
**Proposed method** & **FM-Pre-ResNet + VAE** & \(\mathbf{0.8950\pm 0.0215}\) & \(\mathbf{0.8044\pm 0.0757}\) & \(1.8599\pm 0.6740\) & \(\mathbf{25.6558\pm 16.4001}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of an average DSC, JI, SD, and HD of the state-of-the-art whole heart segmentation methods on MRI images. encoder layers. The segmentation encoder learns a low-dimensional representation of the input, after which VAE reduces the input to a low-dimensional space of 256 (128 to represent std, and 128 to represent mean). A sample is then drawn from the Gaussian distribution with the given std and mean and reconstructed into the input image dimensions following the same architecture as the decoder but without inter-level skip connections. Therefore, VAE acts as a regulator of model weights, adds additional guidance, and exploits the encoder endpoint features.