Investigation of cardiac substructure automatic segmentation methods on synthetically generated 4D cone-beam CT images

Background

STereotactic Arrhythmia Radioablation (STAR) is a novel noninvasive method for treating arrythmias in which external beam radiation is directed towards subregions of the heart. Challenges for accurate STAR targeting include small target volumes and relatively large patient motion, which can lead to radiation related patient toxicities. 4D Cone-beam CT (CBCT) images are used for stereotactic lung treatments to account for respiration-related patient motion. 4D-CBCT imaging could similarly be used to account for respiration-related patient motion in STAR; however, the poor contrast of heart tissue in CBCT makes identifying cardiac substructures in 4D-CBCT images challenging. If cardiac structures can be identified in pre-treatment 4D-CBCT images, then the location of the target volume can be more accurately identified for different phases of the respiration cycle, leading to more accurate targeting and a reduction in patient toxicities.

Purpose

The aim of this simulation study is to investigate the accuracy of different cardiac substructure segmentation methods for 4D-CBCT images.

Methods

Repeat 4D-CT scans from 13 lung cancer patients were obtained from The Cancer Imaging Archive. Synthetic 4D-CBCT images for each patient were simulated by forward projecting and reconstructing each respiration phase of a chosen “testing” 4D-CT scan. Eighteen cardiac structures were segmented from each respiration phase image in the testing 4D-CT using the previously validated platipy toolkit. The platipy segmentations from the testing 4D-CT were defined as the ground truth segmentations for the synthetic 4D-CBCT images. Five different 4D-CBCT cardiac segmentation methods were investigated: 3D Rigid Alignment, 4D Rigid Alignment, Direct CBCT Segmentation, Contour Transformation, and Synthetic CT Segmentation methods. For all methods except the Direct CBCT segmentation method, a separate 4D-CT (Planning CT) was used to assist in generating 4D-CBCT segmentations. Segmentation performance was measured using the Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), and volume ratio (VR) metrics.

Results

The mean ± standard deviation DSC for all cardiac substructures for the 3D Rigid Alignment, 4D Rigid Alignment, Direct CBCT Segmentation, Contour Transformation, and Synthetic CT Segmentation methods were 0.48 ± 0.29, 0.52 ± 0.29, 0.37 ± 0.32, 0.53 ± 0.29, 0.57 ± 0.28, respectively. Similarly, the HD values were 10.9 ± 3.6 , 9.9 ± 2.6 , 17.3 ± 5.3 , 9.9 ± 2.8 , 9.3 ± 3.0 mm, the MSD values were 2.9 ± 0.6 , 2.9 ± 0.6 , 6.3 ± 2.5 , 2.5 ± 0.6 , 2.4 ± 0.8 mm, and the VR Values were 0.81 ± 0.12, 0.78 ± 0.14, 1.10 ± 0.47, 0.72 ± 0.15, 0.98 ± 0.44, respectively. Of the five methods investigated the Synthetic CT segmentation method generated the most accurate segmentations for all calculated segmentation metrics.

Conclusion

This simulation study investigates the accuracy of different cardiac substructure segmentation methods for 4D-CBCT images. Accurate 4D-CBCT cardiac segmentation will provide more accurate information on the location of cardiac anatomy during STAR treatments which can lead to safer and more effective STAR. As the data and segmentation methods used in this study are all open source, this study provides a useful benchmarking tool to evaluate other CBCT cardiac segmentation methods.