Modeling Clinical Tumors to Create Reference Data for Tumor Volume Measurement

Expanding on our previously developed method for inserting synthetic objects into clinical computed tomography (CT) data, we model a set of eight clinical tumors that span a range of geometries and locations within the lung. The goal is to create realistic but synthetic tumor data, with known volumes. The set of data we created can be used as ground truth data to compare volumetric methods, particularly for lung tumors attached to vascular material in the lung or attached to lung walls, where ambiguities for volume measurement occur. In the process of creating these data sets, we select a sample of often seen lung tumor shapes and locations in the lung, and show that for this sample a large fraction of the voxels representing tumors in the gridded data are partially filled voxels. This points out the need for volumetric methods that handle partial volumes accurately.