Evaluation of Semi-automatic Segmentation of Liver Tumors for Intra-procedural Planning

Transarterial chemoembolization (TACE) is a common procedure for the treatment of intermediate-stage primary liver cancer, in which the blood supply of the tumor is suppressed by occluding the supplying vessels. In this procedure, contrast-enhanced cone-beam computed tomography (CBCT) scans are used to localize the tumor lesions and identify their feeding vessels, potentially aided by segmentation software. With the help of semi-automatic segmentation algorithms, a high-quality segmentation of a tumor can be achieved with minimal user input, such as drawing a line approximating the tumor's longest axis. In this paper, we conduct a user study for evaluating human tendencies when annotating tumors in this manner, build a simulator based on our findings and design a semi-automatic segmentation method based on DeepGrow, trained on simulated inputs. We compare it to the random walker algorithm, acting as an established baseline measure, on the task of liver tumor segmentation using a dataset of CBCT scans along with simulated user inputs. We discover that human users tend to overestimate tumors with an average distance of 2.8 voxels to the tumor's boundary. Our customized network outperforms the random walker with an average Dice score of 0.89 and an average symmetric surface distance (ASSD) of 1.16 voxels compared to a Dice score of 0.69 and an ASSD of 2.95 voxels. This shows the potential of learning-based methods to speed up the intraprocedural segmentation workflow.