)1 Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 31000 Osjek, Croatia; [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (K.R. )1 Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 31000 Osjek, Croatia; [EMAIL_ADDRESS] (K.R. )1 Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 31000 Osjek, Croatia; [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (K.R. )1 Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 31000 Osjek, Croatia; [EMAIL_ADDRESS] (K.R. )1 Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 31000 Osjek, Croatia; [EMAIL_ADDRESS] (K.R. )1 Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 31000 Osjek, Croatia; [EMAIL_ADDRESS] (H.L. ); [EMAIL_ADDRESS] (K.R. )1 Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 31000 Osjek, Croatia; [EMAIL_ADDRESS] (K.R. )1 Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 31000 Osjek, Croatia; irena.galic@ferit. for patients with atherosclerosis, congenital heart defects, cardiomyopathy, or even inspecting different heart infections in post-surgical treatment [2]. Whole heart segmentation includes delineation of four heart chambers, the entire blood pool, and the great vessels, which makes manual segmentation by clinical experts time-consuming and prone to observer variability. There is an increasing focus on developing accurate and robust automatic image processing methods. The development of accurate, efficient, and automatic image processing and analysis methods is a complex task, especially in the medical field. The main reason is in high variability in image intensities distribution and dynamic properties of cardiac structures. Nevertheless, the advancements and rapid development in image processing, computer vision, and artificial intelligence fields significantly facilitate this challenging task. A commonly used approach for medical image segmentation includes encoder-decoder-based architectures such as the U-Net architecture [3]. The U-Net architecture and its' corresponding three-dimensional counterpart, 3D U-Net [4], consist of contracting and expanding pathways. Throughout the contracting pathway, the network learns low-level features and reduces its numbers using sets of pooling and convolutional layers. In an expanding pathway, the network learns high-level features and recovers the original image resolution using deconvolutional layers. Features from contracting and expanding pathways are concatenated with skip connections to retrieve lost image information that occurs during the down-sampling process. Intuitively, this indicates that the part of the information is lost during the encoding process and can not be recovered when decoding. Variational autoencoders [5] enable regularization during the training to ensure that the latent space, i.e., encoded space, keeps the maximum of information when encoding, which results in the minimum reconstruction error during the decoding. Furthermore, since the number of features in the contracting pathway is significantly lower than the number in the expanding pathway, direct concatenation of these features may not produce the most optimal results. The increment in the number of layers provides larger parameter space enabling learning of more abstract features. Therefore, deeper architectures could provide more abstract learning that results in better performance and higher accuracy in medical segmentation tasks. Common obstacles in training deeper neural network architectures are the appearance of vanishing gradients, accuracy degradations, and extensive parameter growth that lead to computationally expensive models. Recent advancements have shown that convolutional networks can be significantly deeper and still preserve high efficiency and accuracy if they contain shorter and direct connections in between each layer.