## 1 Introduction  
Texture, and more specifically textural characteristics in images, has been widely studied in the past decades as texture is one of the most important features present in images and can be used for feature extraction [1; 2; 3; 4; 5; 6; 7; 8] and classification and segmentation [9; 10; 11; 12; 13; 14]. The areas of study where texture is present range from crystallographic texture [15], stratigraphy [16; 17], food science of potatoes [18] or apples [19], patterned fabrics [20] to natural stone industry [21]. In medical imaging, there is a large volume of research which exploits the use of texture for different purposes, like segmentation or classification in most acquisition modalities like magnetic resonance imaging (MRI) [22; 23; 24; 25; 26], ultrasound [27; 28], computed tomography (CT) [29; 30; 31], microscopy [32; 33] and histology [34]. There are numerous approaches to texture: Haralick's co-occurrence matrix [4; 5] on the spatial domain, Gabor filters [35; 36; 37] and ordered pyramids [8] on the spectral domain, wavelets [38; 39] or Markov random fields [3; 40].  
In recent years, advances in artificial intelligence have revolutionised image processing tasks. Several deep learning approaches [41; 42; 43] have achieved outstanding results in difficult tasks such as those of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [44]. Convolutional Neural Networks (CNNs) are well suited to analyse textures as their repetitive patterns can be learned and identified by filter banks [45]. The U-Net architecture proposed by [PERSON] [46] has become a very widely used tool for segmentation and analysis reaching thousands of citations in the few years since it was published. U-Nets have been used widely, for instance, for road extraction [47], singing voice separation [48], automatic brain tumour detection and segmentation [49] and cell counting, detection, and morphometry [50]. The success of these deep learning approaches in very different areas invites for their application on texture analysis.  
In this work, a U-Net architecture for the segmentation of textures is implemented and objectively compared against several popular traditional segmentation strategies. The traditional algorithms (co-occurrence matrices [5], watershed [51], local binary patterns (LBP) [52; 53], filtering [54] and multiresolution sub-band filtering (MSBF) [8]) were selected as these have been previously published using the texture composites proposed by [PERSON] [55] and thus an objective numerical comparison is possible.  
To perform an objective comparison, six well-known texture composites from the Brodatz [56] album, first published by [PERSON] and [PERSON] [54], are segmented with U-Nets of different configurations and parameters and the results compared against previously published results. The effects of the configuration of the networks, namely, number of epochs, depth of the network in the number of layers, and type of optimisation algorithm are assessed. All the programming was performed in Matlab(r) (The MathworksTM, Natick, MA, USA) and the code is freely available through GitHub ([[https://github.com/reysaldasoro/Texture-Segmentation](https://github.com/reysaldasoro/Texture-Segmentation)]([https://github.com/reysaldasoro/Texture-Segmentation](https://github.com/reysaldasoro/Texture-Segmentation))).