Takata Takeshi, Yamada Kentaro, Yamamoto Masayoshi, Kondo Hiroshi
Advanced Comprehensive Research Organization, Teikyo University, Tokyo, Japan.
Dotter Interventional Institute, Oregon Health & Science University, Portland, OR, USA.
J Med Syst. 2023 Mar 30;47(1):42. doi: 10.1007/s10916-023-01938-z.
Resuscitative endovascular balloon occlusion of the aorta (REBOA) is an endovascular procedure for hemorrhage control. In REBOA, the balloon must be placed in the precise place, but it may be performed without X-ray fluoroscopy. This study aimed to estimate the REBOA zones from the body surface using deep learning for safe balloon placement. A total of 198 abdominal computed tomography (CT) datasets containing the regions of the REBOA zones were collected from open data libraries. Then, depth images of the body surface generated from the CT datasets and the images corresponding to the zones were labeled for deep learning training and validation. DeepLabV3+, a deep learning semantic segmentation model, was employed to estimate the zones. We used 176 depth images as training data and 22 images as validation data. A nine-fold cross-validation was performed to generalize the performance of the network. The median Dice coefficients for Zones 1-3 were 0.94 (inter-quarter range: 0.90-0.96), 0.77 (0.60-0.86), and 0.83 (0.74-0.89), respectively. The median displacements of the zone boundaries were 11.34 mm (5.90-19.45), 11.40 mm (4.88-20.23), and 14.17 mm (6.89-23.70) for the boundary between Zones 1 and 2, between Zones 2 and 3, and between Zone 3 and out of zone, respectively. This study examined the feasibility of REBOA zone estimation from the body surface only using deep learning-based segmentation without aortography.
主动脉复苏性血管内球囊阻断术（REBOA）是一种用于控制出血的血管内手术。在REBOA中，球囊必须放置在精确的位置，但该操作可以在没有X线透视的情况下进行。本研究旨在利用深度学习从体表估计REBOA区域，以实现球囊的安全放置。从开放数据库中收集了总共198个包含REBOA区域的腹部计算机断层扫描（CT）数据集。然后，对从CT数据集中生成的体表深度图像以及与这些区域对应的图像进行标注，用于深度学习训练和验证。采用深度学习语义分割模型DeepLabV3+来估计这些区域。我们使用176幅深度图像作为训练数据，22幅图像作为验证数据。进行了九折交叉验证以概括网络的性能。1-3区的中位Dice系数分别为0.94（四分位间距：0.90-0.96）、0.77（0.60-0.86）和0.83（0.74-0.89）。1区和2区之间、2区和3区之间以及3区与区域外之间边界的中位位移分别为11.34 mm（5.90-19.45）、11.40 mm（4.88-20.23）和14.17 mm（6.89-23.70）。本研究检验了仅使用基于深度学习的分割而不进行主动脉造影从体表估计REBOA区域的可行性。
