# MedicalSeg: A Medical GUI Application for Image Segmentation Management
## 1 Introduction  
In the most recent decades, one of the most common purposes in medical imaging field is focused on image segmentation to obtain regions of interest (ROI) through a semi-automatic or automatic process [1]. Segmentation is one of the most important and popular tasks in medical image analysis, which plays a critical role in disease diagnosis, surgical planning, and prognosis evaluation. In fact, it is an essential step in computer-aided diagnosis systems in different applications [2]. Image segmentation is considered one of the main pre-processing steps in many applications, because a segmented section is necessary in many instances to analyse and extract certain features for post-processing purposes. New advances in medical imaging modalities, such as X-ray, magnetic resonance imaging (MRI), ultrasound (US), computed tomography (CT), or nuclear medicine request the implementation and validation of novel segmentation algorithms [3]. According to different image modality, the main objective is to develop applications for differentaspects, such as edge detection, location of organs, segmentation of anatomical regions, or lesion detection. The challenge is how to extract global features of the image to distinguish ROI from the background. The main limitation is that there is no algorithm that is suitable for all the cases. Moreover, it could be a tedious and inefficient procedure applied for large image database. In this sense, there have been various approaches proposed to the task of segmenting ROIs in medical images using traditional segmentation algorithms. A comprehensive review of the top methods in 10 3D medical image segmentation challenges during 2020 was studied, covering a variety of tasks and datasets [4]. Challenges provide an open and fair platform for various research groups to test and validate their segmentation methods on common datasets acquired from the clinical environment. Nowadays, one of the main focuses in the computer vision (CV) field is based on artificial intelligence (AI) algorithms for segmentation and classification in image domain such as machine learning (ML) and deep learning (DL) [5]. ML forgoes the traditional programming paradigm where problem analysis is replaced by a training framework, where the system is fed a large number of training patterns (sets of inputs for which the desired outputs are known) which it learns and uses to predict new patterns [6]. Additionally, DL is a subset of machine learning, based largely on Artificial Neural Networks (ANNs), a computing paradigm inspired by the functioning of the human brain. The main benefit of DL approach is that the architecture learns both the feature extraction and the classification, thus releasing the need of tailored features defined by an expert. The advent of DL may open a new paradigm to do something with traditional techniques to overcome the many challenges DL brings (e.g., computing power, time, accuracy, characteristics, and quantity of inputs, among others). Certainly, they need a lot of domain expertise, human intervention only capable of what they are designed for, and comparison with the traditional segmentation algorithms, such as thresholding, edge detection, clustering, region growing, or active contour models, provide more promising solutions [3]. However, in both cases, the solving of all CV problems and the usability of traditional algorithms still have a relevant importance. In addition, the development of supervised AI algorithms requires a large amount of ground truth databases validated by radiologists and medical experts to ensure an efficient solution. In order to obtain these data, robust applications are required to generate this step to be used in following purposes. Moreover, many repositories including medical applications have been published [7]. The challenge is to develop applications for different biomedical segmentation purposes and image modalities.