# MedicalSeg: A Medical GUI Application for Image Segmentation Management
## 5 Conclusions  
A new application based on different segmentation techniques using medical databases is presented. The management of MedicalSeg is developed in three main parts: (i) image segmentation development, (ii) the design of a GUI interface, and (iii) management and analysis. The image segmentation tool contains a set of nine segmentation techniques, such as threshold and normalization segmentation, Otsu, ThreshMAX percentage segmentation, manual and automatic Canny edge threshold segmentation (manual and automatic), intensity threshold segmentation, active contour model (snake segmentation), Watershed and GMM. The GUI interface is described and a study case of its main functionalities is detailed. Moreover, the analysis of the results are included In order to evaluate this application, five medical datasets (mammography, prostate, retinal, ultrasound, and brain vascular) are tested to prove the usability of this tool. A validation procedure and performance analysis are presented in this work.  
MedicalSeg tool demonstrates that the use of the proposed segmentation techniques are useful for medical imaging. A comparison among all the algorithms discussing the advantages and disadvantages of each dataset is performed. At this moment, it has been implemented nine segmentation techniques, but the purpose is continue working on to add more processing tools. MedicalSeg is designed as an open-source platform. In this sense, developer users can add new segmentation techniques to this application being an  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Images** & **Thres** & **ThMAX** & **Otsu** & **CannyAuto** & **Intensity** & **Snake** & **Watershed** & **GMM** & **All** \\ \hline
1 & 0.73 & 1.15 & 0.76 & 1.89 & 1.27 & 1.03 & 2.35 & 3.61 & 3.61 \\
10 & 1.04 & 2.09 & 3.84 & 12.79 & 2.46 & 1.81 & 4.39 & 18.19 & 18.19 \\
20 & 2.78 & 3  
advantage compared with other medical applications. For this reason, users can adapt to their purposes and evolved methods offered by frameworks such as ITK, among others. The inclusion of segmentation techniques based on convolutional neural networks, such as U-net, could be a good improvement to this tool.  
In conclusion, the intention is not to use this tool just to compare segmentations, and creates a tool that substitutes others medical applications. Indeed, the purpose is not trying to compare MedicalSeg with other medical applications that are available online. However, a comparison with reference applications, such as ImageJ, is performed to demonstrate the robustness of the proposed work. Rather, we want users to be able to take advantage of a tool that can choose different models and different techniques on the same time and compare the obtained results. Moreover, the easy interface that automatically save the results in folders facilitating the management of data that could be used for post-analysis purposes. We intend to offer users all these advantages proposed in our work. The interest is always to share knowledge and help the scientific community, in particular in the medical field.  
Conceptualization, C.M. and R.B.; software, C.M.; methodology, all authors; validation, all authors; resources, J.M. and A.L.; data curation, C.M., J.M., and A.L.; writing--original draft preparation, C.M.; writing--review and editing, all authors; visualization, all authors; supervision, R.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by the Spanish Ministry of Economy and Competitiveness [Project PID2020-116927 RB-C22, Agencia Estatal de Investigacion funds]. We would like to express our special thanks of gratitude to [PERSON] (Hospital of Dijon, France) to use a set of prostate database for testing.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.