Needle Localization and Segmentation for Radiofrequency Ablation of Liver Tumors under CT Image Guidance

Radiofrequency ablation (RFA) of liver cancer under computer tomography (CT) guidance is a minimally invasive procedure in which CT images are utilized to guide the physician in introducing the needle into the target lesion. However, the adequate visualization of the needle and anatomy is hampered by the 2D slide based-view used in the current clinical practice. Thus, due to the lack of 3D information, the physician requires high experience and more interaction with the guidance systems to envision the needle's position in the liver, which is inconvenient in clinical practice. In this study, we propose a method for robust needle segmentation using CT images to improve the visualization of the needle during the intervention. The method utilizes a convolutional neural network (CNN) to detect the needle in orthogonal 2D projections of the CT image to construct the needle volume of interest (VOI). Subsequently, a patch-based 3D CNN is applied to segment the needle. We evaluate the method's accuracy using Dice score (DSC), Hausdorff distance (HD), the needle shaft error Eshaft, and needle tip error Etip.. The results show that the proposed method achieves the means of DSC, HD, Etip, Eshaft and processing time of 0.89, 3.3 mm, 0.9 mm, 0.43 mm, and 2.6 seconds, respectively. We conclude that the proposed method is feasible for improving needle visualization in the interventional room.