Introduction: Segmentation represents a crucial part of the analysis of medical image data and enables precise identification of anatomical or pathological structures in CT images. In this field, various approaches exist, including manual and automatic methods, where artificial intelligence procedures, among deep learning, are used.Their fundamental component is neural networks. For the purpose of segmentation, there are several different software solutions available on the market. Purpose: We aimed to systematically present various segmentation methods in medical imaging diagnostics and compare the user effectiveness of the commercial software tool Syngo.via with open-source tools 3D Slicer and MONAI Label. Methods: In the theoretical part, we employed a descriptive method by reviewing domestic and foreign literature in the field of artificial intelligence and medicine. The second part was experimental and was practically conducted using various software tools. In these tools, we performed segmentation of anatomical structures on a chest CT scan and compared their practical usability and user experience. Results: Manual segmentation requires considerable skill and knowledge and is also time-consuming. Despite its extremely user-friendly nature, Syngo.via is limited in functionalities compared to other advanced software tools for segmenting CT images. The segmentation accuracy in MONAI Label is adequate and comparable to manual segmentation while requiring less user interaction, less knowledge, and is executed incomparably faster than manual segmentation. Segmentation in 3D Slicer was more challenging than using MONAI Label, as it required more user interaction and knowledge of anatomy, which also affects the duration of the process, which is more time-consuming in all cases compared to MONAI Label. Discussion and conclusion: Syngo.via represents a highly advanced program for processing and analyzing medical images, standing out for its exceptional performance and user-friendly nature. However, it is associated with high costs and limitations regarding new functionalities that need to be upgraded and paid for, as it is owned by Siemens Healthineers. On the contrary, open-source platforms like 3D Slicer and MONAI Label offer free usage, broader accessibility, and participation in the research community, as well as transparency of operation. This enables the creation of precise models that can be adapted to the specific needs and challenges of various clinical or research cases.
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