Discussion regarding Segmentation

Discussion with Sudhir regarding Segmentation

When a tumor is present inside a DICOM dataset, segmentation helps us separate that tumor by outlining it from the rest of the tissues so we can look at it more clearly. We discussed about to start with 2D segmentation, where we look at each slice of the scan one by one (like flipping through pages of a book) and outline the area where the tumor is present. The process typically starts with 2D segmentation, where each DICOM slice (axial, coronal, or sagittal) is analyzed individually to detect and outline the tumor boundaries. This can be achieved AI-based algorithms to create accurate 2D segmentations for slices At this stage, the complex thing would be is to build a complex polygonal mesh/object that represents the tumor shape in 3D or use a “shrink-wrap” method that wraps a smooth outer layer around the segmented region to form a realistic 3D model.