# Mixed-Sized Biomedical Image Segmentation Based on U-Net Architectures
## 1 Introduction  
Technological progress and advanced tools for medical analysis have significantly contributed to reducing waiting times for the diagnosis of various diseases. In particular, in oncology, the increase in the number of screening tests associated with the drastic decrease in diagnosis times has contributed to significantly reducing the mortality rate of diseases. Since the late 1980s, diagnostic imaging has been essential to visualizing organs and tissues in detail in order to detect tumors even in their early stages [1; 2]. However, the tomographic images of the human body, obtained by CT-Scan (Computed Tomography), require a specialist to manually identify and segment the area of interest. In the field of image processing, segmentation is defined as the process of decomposing an image into its constituent regions or into the objects that compose it [3]. Since the manual approach to segmentation, still used by a large part of the medical staff, is time-consuming and tedious, some techniques have been proposed to make it automatic. This can be done on the basis of certain criteria concerning the pixels belonging to a region. This is a complex objective, also because the accuracy of the result depends on the type of information to be extracted from the image. This paper shows a solution, based on Artificial Intelligence (AI) technologies, to automate, speed up and possibly improve the analysis of the images compared to what a human operator could do. This tool incorporates and enhances some known results based on deep learning. In particular, it is based on the use of a deep convolutional neural network (CNN) that allows one to automatically process and analyze multi-scale digital images, known in the literature as U-Net [4]. Our solution includes image processing techniques that improve visualization in terms of quality, such as increasing contrast and provide a valuable toolfor the visual analysis of specific morphological characteristics of objects present within the image, such as their perimeter and area. In more detail, the main focus of our research is the definition of an appropriate automatic segmentation method based on the current state-of-the-art and the comparison of its performance across multiple types of images at different scales. The developed neural network allows processing and identifying a wide typology of anatomical organs, following an adequate training of the model using the CT-Scans of the patients. This feature determines a very versatile algorithm behavior, extensible to any organ. However, if the loss function used to drive the training process cannot suitably capture the information present in the mask of the organs at different scales, the resulting prediction could be unsatisfactory in operation. The main contribution of this paper is to show how to improve performance in the presence of target images characterized by very sparse signals without significantly improving the cost of the learning process and without introducing different learning algorithms. Furthermore, by making slight changes to the model parameters, the algorithm could be used not only in the medical field, but also in other fields that require semantic segmentation of images. In fact, the applied techniques have universal value, although in this paper they have been treated in relation to the prefixed purpose. The contribution of this paper is twofold:  
* _First_, a performance analysis of the baseline segmentation model on different types of organs is shown. In some case studies, the model provides good results. However, the performance is often unsatisfactory when small organs or restricted regions of interest, which are important in the diagnosis of serious pathologies, are considered [5]. * _Then_, a proposal for improving multi-scale segmentation, based on lightweight image preprocessing is shown. This proposal leads to 20% improvement in the score evaluated by using a metric based on the Dice coefficient [6], also known as F1-score [7].