20. IMPLEMENTATION OF A SEMI-AUTOMATED SEGMENTATION TOOL FOR COLON SURGICAL PLANNING

Abstract

Abstract Introduction This study outlines the design and implementation of an application focused on optimizing surgical planning for colon surgery through the automated segmentation process of Computed Axial Tomography (CT) images. The tool provides continuous assistance to the user, enhancing the overall process management and efficiency. Methods A module has been developed for the 3DSlicer image computing platform. The algorithm was programmed in Python, primarily utilizing the Slicer, MRML, and VTK libraries. Additionally, DICOM images obtained from a publicly available database were used as samples for the study. Results The implementation of the semi-automatic segmentation method has proved to be successful, achieving the creation of a three-dimensional (3D) model of the colon with satisfactory results. The user has the facility to upload a CT image from a DICOM study, and through few interactions, a 3D model of the colon is generated, optimizing the surgical planning process. Conclusion Although we have successfully developed a semi-automatic segmentation method, the process requires certain specific conditions. For the image to be interpreted correctly, the patient must be treated with iodinated contrast medium prior to the CT study. In addition, an insufflation of the colon should be performed. These prerequisites are essential to ensure the quality and accuracy of the resulting images, which in turn will impact the efficiency of the planning process.