REBOA Zone Estimation from the Body Surface Using Semantic Segmentation.
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 usin...
| Published in: | Journal of Medical Systems Vol. 47; no. 1; pp. 1 - 10 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Dec2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162829425&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162829425 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Dec2023 vid: 47 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 162829425 10.1007/s10916-023-01938-z 162829425 ppf: 1 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: REBOA Zone Estimation from the Body Surface Using Semantic Segmentation. aug: au: Takata, Takeshi Yamada, Kentaro Yamamoto, Masayoshi Kondo, Hiroshi affil: Advanced Comprehensive Research Organization, Teikyo University, Tokyo, Japan sug: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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