## 1 Introduction
### Background and Related Work  
#### 1.1.1 Deep Learning Segmentation Methods  
Current state of the art methods for segmenting image data are deep learning based. A comparison of most currently used network architectures is described in [1]. [PERSON] et al. [2] proposed the first  
Figure 1: Example of MRI sagittal brain scan slice (**left**) and CT transversal thoracic scan slice (**right**)—tissue segmented with our system is highlighted in yellow.  
fully convolutional architecture for segmentation. This work laid foundations for most currently used segmentation neural network architectures, because using only convolutional layers made a significant rise in accuracy. However, this architecture needs a large dataset to be fully trained and therefore it is not usually suitable for medical image segmentation. Using only convolutional layers combined with skip connections became one of the most popular architectures the autoencoder type of network called U-Net [3]. It consists of downsampling, bridge and upsampling blocks and is widely used for segmenting medical data thanks to its ability to be properly trained using little data comparing to its competitors such as [2]. Development in the area of classification networks using data interconnections from coarse to fine layers of the network as in residual networks [4] and followed by densely connected networks [5] led to application of this principle also in segmentation networks. Popular segmentation architecture using these principles is Tiramisu network [6]. All cited architectures are designed for 2D data. Reason for 2D data processing is that these networks were primarily published to be used on general image data and also because the training is implemented for GPU. GPU is used for parallel data processing and can achieve 20 or more times faster computation, but with limited memory use. With the latest advancements in GPU technology we are able to train larger architectures due to increased GPU RAM memory and utilize information in the third dimension for higher accuracy, because most medical data that are used in clinical practice consist of 3D image volumes.  
Implementation of U-Net network for 3D data processing was done in [7] and achieved higher accuracy on testing data than original U-Net. Another design of 3D segmentation U-Net type network was done in [8]. Combining 2D and 3D data processing in a hybrid densely connected neural network architecture was done in [9] These methods however are designed to process medical images in lower resolution and therefore are not suitable for processing high resolution thoracic and other upper body medical images. The implementation and evaluation of residual and dense interconnections in the 3D U-Net segmentation model is the main proposal of this paper. We tested both Residual-U-Net and Dense-U-Net architectures and optimized them for medical image processing in 3D.  
In contrast with previously mentioned architectures, our goal was to design U-Net architecture with densely connected layers for 3D data processing optimized for processing data in high resolution and compare its accuracy with the results of the original U-Net implementation and U-Net with added residual connections.