TY - JOUR
T1 - REBOA Zone Estimation from the Body Surface Using Semantic Segmentation
AU - Takata, Takeshi
AU - Yamada, Kentaro
AU - Yamamoto, Masayoshi
AU - Kondo, Hiroshi
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
KW - Balloon placement
KW - Deep learning
KW - Endovascular
KW - REBOA
KW - Semantic segmentation
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U2 - 10.1007/s10916-023-01938-z
DO - 10.1007/s10916-023-01938-z
M3 - Article
C2 - 36995484
AN - SCOPUS:85151472832
SN - 0148-5598
VL - 47
JO - Journal of Medical Systems
JF - Journal of Medical Systems
IS - 1
M1 - 42
ER -