## 3 Results
However, it is impossible to separate them correctly for this type of application where a tumor with contrast is very similar to other organs. In the case of ITK-Snap, it has a seed tool very similar to the \"Grown from seed\" tool in 3D Slicer but with the difference that it allows you to choose how many iterations you need and how far to grow the section. But as in the case of this section of the tumor, you need to delimit its volume using other neighboring organs; additionally, you need a Boolean cutting tool like the one that 3D Slicer has, but this software lacks. In the case of 3D Slicer, the segmentation of the healthy part of the lung and the tumor section has been able to work correctly. For the healthy part, as in the other two software, a threshold was used to generate the model; the difference lies in the fact that it has a local histogram, which allows us to more accurately delimit the Hounsfield Units in which the section is found to segment. For the segmentation of the tumor section, it was required to use multiple tools. It starts with finding the appropriate threshold for the bones and liver, accompanied by the healthy section that was previously created, then with the segmentation of the tumor through seeds which gives us an approximate result of the tumor but without being exact. For this reason, all of them were intersected using the previously created models to perform a Boolean operation and obtain a final model. One of the disadvantages of using this software is that the exported models have many conflicting faces and edges that, when using 3D printing laminating software, are recognized as model errors. Most laminating software can correct these errors to render the 3D model correctly. Thanks to this multiplicity of tools, a complex case such as this syndrome can be correctly segmented. Within the selection criteria of these three software was the one that could carry out the segmentation process without having programming codes involved but instead having a graphical interface that does not require specialized knowledge. However, some of these software require an amount of more significant steps to obtain similar results as is the case with ITK-Snap since when using the Snake Mode, you must add an unknown amount of bubbles to determine estimates of the area and then wait for the software to make a set of iterations to cover the sections with similar contrast. The opposite case is Inveslius, where the steps have a direct order, making it suitable for those unfamiliar with its interface, thus optimizing the work time consumed. The use and comparison of these open-source tools for segmentation have been used in multiple parts of the body, such as bones, as is the case of the work of [PERSON] et al. [11], where they analyzed as points of comparison the weight and quantity of triangles of the exported models. There are also antecedents in the literature of its use in tumor sections located in the brain, such as glioblastomas; an example is an article presented by [PERSON] et al. [12], where it is concluded that the best performances at the level of time expenditure are those of 3D Slicer and ITK-Snap. But in the literature, there are no reports of using this software for a rare syndrome like the one presented in this study, where the tomography contrast is not very different from other organs. Previous reports highlight the importance of SFT associated with Doege-Potter syndrome [4, 13, 14, 15], showing no differences between benign and malignant cases and that all end up in surgery as an effective treatment [16]. Surgical activities use images such as CT as an aid tool and can benefit from 3D modeling by allowing us to approximate the actual size of the tumor, as seen in other medical areas [17, 18]. In this sense, free-access software can give oncology a better chance at facing all stages of cancer patient care. The use of 3D modeling is key to understanding the size of the tumor as well as its surgical margins.