Automatic iceball segmentation with adapted shape priors for MRI-guided cryoablation.

The Incidence of renal cell carcinoma in the United States has increased by 126% over the past 50 years (1), together with an increase in its associated mortality rates (2). Image-guided percutaneous cryoablation is one of the most promising and prevailing minimally invasive techniques for treating kidney cancer (3–5). Compared with computed tomography (CT), MRI is considered a superior intraprocedural imaging tool to visualize the ablation zone, referred to as the “iceball”, the tumor, and, nearby anatomical structures (6). The current cryoablation procedure relies on the interventionalist’s constant qualitative assessment of repeatedly acquired intraprocedural MR images during the 15-min freezing cycle. The interventionalist has to evaluate the coverage of tumor and surrounding normal parenchyma by the slowly growing iceball, while at the same time tending to other procedural needs, such as keeping the skin entry site warm and monitoring patient’s medical status. At times, this may lead to inaccurate or incomplete interpretation of the progress of the ablation. Also, it could cause deficient tumor coverage that would later lead to recurrence, and injury to the surrounding critical structures with subsequent complications (7). Hence, there is a need for a computerized monitoring tool to aid the interventionalist by providing quantitative tumor ablation metrics and three-dimensional (3D) visualization of the iceball. One key component of such a tool is fast and accurate segmentation of the 3D iceball configuration, which is displayed as an approximately ball-shaped signal void region in MR images. The accuracy of many semi-automatic segmentation tools, such as the ITK-SNAP (www.itksnap.org) (8) and the GrowCut module (9) in 3D Slicer (www.slicer.org) (10), is proportional to the manual operations involved, e.g., the inputting of fiducials and the extent of the segmented region growth, and interactive editing until segmentation is complete. The time required to perform these manual operations makes semiautomatic approaches impractical for intraprocedural segmentation during cryoablation. Automatic iceball segmentation is challenging, as the abdominal anatomy surrounding the iceball, the probe artifacts, and the iceball itself have similar intensity values. This causes the failure of segmentation methods relying solely on intensity information. The approach of Liu et al (11) using graph cut (12–16) with shape priors (17–19) generated from probe locations (named GC-prior) showed promise. However, directly using the modeled iceball shape based on probe locations in the baseline scan is vulnerable to errors, such as those caused by inaccurate probe localization, imperfection of iceball shape modeling, or misregistration between the baseline scan and images obtained during the freeze cycles due to patient’s movement. The purpose of this study is to develop an automatic iceball segmentation method that is robust to the predetected probes, so that the overall accuracy of the segmentation can be improved. Different from previous work (11), our segmentation method is initialized with semiautomatically detected 3D cryoablation probes rather than manually labeled 2D probes. The former probe localization method is considered more suitable for a clinical setting. We incorporated a pre-segmentation step using the Fuzzy C-means (FCM) classification paradigm (20), and a shape prior adaptation step using a Fast Marching propagation process (21,22). These steps provide an adapted iceball shape for modeling the priors, as well as initial intensity centroids, both to be input into the graph cut segmentation. In addition, we introduce intensity normalization and noise reduction steps for better segmentation results. Finally, this study reports results based on a wider selection of cases (13 subjects) compared with the two cases reported in (11).