Prof. Ryo Ohtera Profile

Prof. Ryo Ohtera

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Proceedings Article | 2 May 2024 Paper

Aortic injury detection from CT images using convolutional neural network

Mayu Wakamori, Shunsuke Takahara, Ryo Ohtera

Proceedings Volume 13164, 131642A (2024) https://doi.org/10.1117/12.3018917

KEYWORDS: Computed tomography, Object detection, Deep learning, Image processing, Convolutional neural networks

Read Abstract +

Thoracic aortic injury is a critical condition with life-threatening implications, posing a significant threat to one's survival. When a patient is taken to the hospital, it is crucial to promptly identify the injury. The diagnosis of this injury is conducted with CT images.

In this study, we examine the effectiveness of deep learning methods in the accurate detection of aortic injury. We used YOLOX and YOLOv8 models as deep learning methods to detect the aortic injury. In the experiments, our findings highlight the successful identification of the site of injury and the application of the “injured” label to CT images of aortic injury. Finally, our study realized high-accuracy detection not only for Contrast-enhanced CT images but also for Plain CT images without the contrast mediums.

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