## 2 Related Concepts
### Previous Methods for Whole Heart Segmentation  
The field of whole heart segmentation is a frequently researched area due to its extremely high importance in clinical practice. Various segmentation algorithms and methods have been proposed over the years. Detail reviews of previously published methodologies can be found in [7; 8; 9; 10]. Prior methods are fundamentally classified into traditional segmentation methods (active contours, deformable models, registration, atlas-based frameworks) and CNN-based segmentation methods. For example, [PERSON] et al. [11] propose a new global atlas ranking scheme where both the global and local atlases use the theoretical measures information for computing similarity between the atlases and target image. [PERSON] et al. [12] propose a method that combines topological graphs and local probabilistic atlases for learning priors. At the same time, the hidden Markov field (MF) integrates the learned information and provides final pixel classification. A significant limitation of registration-based methods is a requirement for high similarity in volume orientation and acquisition protocols between target and atlas images. Recent advancements introduce a simple linear iterative clustering (SLIC) super voxel method for prevention of misregistration by detecting a bounding box region enclosing the heart, after which heart segmentation is performed subsequently [13]. Although registration and atlas-based frameworks usually have high accuracy and precision, algorithms are still not robust enough, which often leads to unsatisfactory results when the data quality is poor.  
Recently, CNN-based methods have shown superior performance in the field of medical image segmentation and analysis. For example, [PERSON] et al. [14] use two separate CNNs: first to localize heart and second to segment the fine details of the heart structures within a small region of interest (ROI). The localization network predicts the approximate center of ROI using heatmap regression [15] and the U-Net. Final pixel predictions are acquired using the multi-label segmentation CNN. [PERSON] et al. [16] combine statistical shape priors with CNNs to extract 3D shape knowledge using the shape context method. They detect ROI using a random forest landmark detection, after which they generate a probability map using multi-view slices and three 2D U-Nets. Finally, they apply a hierarchical shape prior algorithm [17] on the probability map to estimate the shape of each heart structure.  
[PERSON] et al. [18] use 2D CNNs with a multi-planar method to investigate the power of retaining spatial information across slices, as is the case of 3D networks. [PERSON] et al. [19] present a multi-planar CNN method using an encoder-decoder architecture. After that, they apply an adaptive fusion [20] to obtain refined segmentation. This method requires less memory in comparison to the 3D counterpart. [PERSON] et al. [21] propose a multi-modality transfer learning method that combines spatial attention mechanism for retaining and removing useful and redundant information, respectively. Furthermore, [PERSON] et al. [22] apply deep supervision during network training to obtain faster convergence and higher distinguishing ability, while [PERSON] et al. [23] combine ROI detection and 3D deeply-supervised U-Net to reduce the computational complexity. Although convolutional neural networks perform well, the limited amount of annotated data requires the development of efficient and more complex, deeper network architectures.