Related Experiment Video
Updated: May 20, 2025
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Technical implications of a novel deep learning system in the segmentation and evaluation of computed tomography
1Department of Cardiothoracic Surgery, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.
Objective:
The goal of this study was to compare the computed tomography angiography scans of the segmentation results from the Cvpilot, 3mensio, and Volume Viewer systems to explore the practicability of the Cvpilot system in the automatic segmentation and technical evaluation of the aortic root before transcatheter aortic valve replacement (TAVR).
Design:
A total of 154 patients who underwent TAVR at our center from January 2022 to May 2023 were enrolled, and their computed tomography angiography images were analyzed using the Cvpilot, 3mensio, and Volume Viewer systems, respectively.
Setting:
Not applicable.
Participants:
Not applicable.
Main Outcome Measures:
The reconstructed computed tomography angiography images were evaluated by experts, and the measurements of the aortic roots were analyzed statistically.
Results:
Compared with the 3mensio system, 92.2% of patients (n = 142) evaluated with the Cvpilot system reached grade A, 5.2% of patients (n = 8) reached grade B, and 2.6% of patients (n = 4) reached grade C. Compared with the Volume Viewer system, 90.9% of patients (n = 140) evaluated with the Cvpilot system achieved grade A, 7.1% of patients (n = 11) achieved grade B, and 2.0% of patients (n = 3) achieved grade C. Furthermore, there was no significant difference among the measurement results of the Cvpilot, 3mensio, and Volume Viewer systems (all p > 0.05).
Conclusion:
Overall, the Cvpilot system is effective and reliable. It can accurately complete the segmentation and the measurement of aortic root structures, thereby effectively improving the measurement quality before TAVR.
Trial Registration:
Not applicable.
