# Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI
## 1 Introduction  
Radiation therapy (radiotherapy) is a cancer treatment that uses ionizing radiation to kill cancer cells or control the growth of tumours. It is a very common procedure to treat all stages of prostate cancer. However, this procedure can also damage the normal cells around the cancer cells putting the surrounding organs at risk of post-treatment complications [1]. In the case of prostate cancer, the main objective is to deliver a maximum dose of radiation to the prostate and minimise the dose received by the bladder and rectum [2]. For this reason, accurate prostate segmentation is required. Manual labelling of an organ can be a time consuming and very challenging process. It involves one or more experts scanning through the dataset and labelling the organ. As a result, labels produced by experts are usually subject to intra- and inter-expert variability as a result of the varying expertise levels [3], i.e., an expert may segment a specific image differently, if done more than once, or different experts may segment the same image differently [4]. Automatic segmentation can speed up the segmentation process as well as minimise the intra- and inter-expert variability problem [5; 6]. In order to perform automatic prostate segmentation on MRI (Magnetic Resonance Imaging) images there are two main methods traditionally employed: atlas-based and deformable model-based methods [7]. In the atlas-based method, a set of images and their corresponding labels are combined together after non-rigid registration (NRR) to create a reference atlas and a corresponding labelled structure. The atlas image, in this case, contains the prostate and its surrounding tissue with the corresponding labelled structure representing the probability of a voxel being a part of the prostate. The NRR of the atlas to the new, unseen MRI scan is used to obtain the segmentation of the prostate of a new patient [7]. In the deformable model-based method, a good initialisation of the model is required. The model can be initialised by atlas-based segmentation [7; 8], where a surface is extracted from a thresholded probabilistic segmentation and the model is deformed to closely match the organ boundary by the use of the grey-level information of the image. The grey-level model is developed offline with one-dimensional grey-level profiles taken along the normals of each vertex of the surface for the images. A distance metric is then used to match the profiles of the model and the profiles extracted from the case image [7]. As both methods either rely on the atlas-based method or good initialisation, they are prone to errors [9] and can be time consuming [7]. In recent years, machine learning-based algorithms have made positive contributions in prostate segmentation tasks [10; 11; 12; 13]. Machine learning algorithms have the ability to automatically detect different patterns from the given data or information provided to the model [14]. Deep learning is a class of machine learning algorithms that model high-level abstraction by using several processing layers of transformations [15]. It uses an architecture of multi-level linear and non-linear operations (i.e., layers) to learn complex functions that can represent high-level abstractions [16]. It automatically learns hierarchical features of an input that carry different semantics on different levels [16]. Unlike the traditional machine learning algorithms, this feature-learning ability allows the system to learn complex functions that map the input to the output directly from the data without relying on handcrafted features [17]. Deep learning algorithms based on the convolutional neural network (CNN) such as the Fully Convolutional Network (FCN) [18], U-Net [19] and DenseNet [20] have achieved outstanding results in prostate segmentation tasks [21; 22; 23; 24; 25; 26; 27]. The CNN utilises a number of convolutional and pooling layers for extracting features automatically.