Inomata Soichiro, Yoshimura Takaaki, Tang Minghui, Ichikawa Shota, Sugimori Hiroyuki
Graduate School of Health Sciences, Hokkaido University, Sapporo 060-0812, Japan.
Department of Health Sciences and Technology, Faculty of Health Sciences, Hokkaido University, Sapporo 060-0812, Japan.
J Cardiovasc Dev Dis. 2024 Dec 25;12(1):3. doi: 10.3390/jcdd12010003.
This study evaluates the use of deep learning techniques to automatically extract and delineate the aortic valve annulus region from contrast-enhanced cardiac CT images. Two approaches, namely, segmentation and object detection, were compared to determine their accuracy.
A dataset of 32 contrast-enhanced cardiac CT scans was analyzed. The segmentation approach utilized the DeepLabv3+ model, while the object detection approach employed YOLOv2. The dataset was augmented through rotation and scaling, and five-fold cross-validation was applied. The accuracy of both methods was evaluated using the Dice similarity coefficient (DSC), and their performance in estimating the aortic valve annulus area was compared.
The object detection approach achieved a mean DSC of 0.809, significantly outperforming the segmentation approach, which had a mean DSC of 0.711. Object detection also demonstrated higher precision and recall, with fewer false positives and negatives. The aortic valve annulus area estimation had a mean error of 2.55 mm.
Object detection showed superior performance in identifying the aortic valve annulus region, suggesting its potential for clinical application in cardiac imaging. The results highlight the promise of deep learning in improving the accuracy and efficiency of preoperative planning for cardiovascular interventions.
本研究评估使用深度学习技术从对比增强心脏CT图像中自动提取和勾勒主动脉瓣环区域。比较了两种方法，即分割和目标检测，以确定它们的准确性。
分析了一个包含32例对比增强心脏CT扫描的数据集。分割方法使用了DeepLabv3+模型，而目标检测方法采用了YOLOv2。通过旋转和缩放对数据集进行扩充，并应用五折交叉验证。使用Dice相似系数（DSC）评估两种方法的准确性，并比较它们在估计主动脉瓣环面积方面的性能。
目标检测方法的平均DSC为0.809，显著优于分割方法，分割方法的平均DSC为0.711。目标检测还表现出更高的精度和召回率，假阳性和假阴性较少。主动脉瓣环面积估计的平均误差为2.55mm。
目标检测在识别主动脉瓣环区域方面表现出卓越性能，表明其在心脏成像临床应用中的潜力。结果突出了深度学习在提高心血管介入术前规划的准确性和效率方面的前景。
