Show, Attend and Detect: Towards Fine-Grained Assessment of Abdominal Aortic Calcification on Vertebral Fracture Assessment Scans

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dc.contributor.authorGilani, S.Z.
dc.contributor.authorSharif, N.
dc.contributor.authorSuter, D.
dc.contributor.authorSchousboe, J.T.
dc.contributor.authorReid, S.
dc.contributor.authorLeslie, W.D.
dc.contributor.authorLewis, J.R.
dc.contributor.conference25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (18 Sep 2022 - 22 Sep 2022 : Singapore)
dc.contributor.editorWang, L.
dc.contributor.editorDou, Q.
dc.contributor.editorFletcher, P.T.
dc.contributor.editorSpeidel, S.
dc.contributor.editorLi, S.
dc.date.issued2022
dc.description.abstractMore than 55,000 people world-wide die from Cardiovascular Disease (CVD) each day. Calcification of the abdominal aorta is an established marker of asymptomatic CVD. It can be observed on scans taken for vertebral fracture assessment from Dual Energy X-ray Absorptiometry machines. Assessment of Abdominal Aortic Calcification (AAC) and timely intervention may help to reinforce public health messages around CVD risk factors and improve disease management, reducing the global health burden related to CVDs. Our research addresses this problem by proposing a novel and reliable framework for automated “finegrained” assessment of AAC. Inspired by the vision-to-language models, our method performs sequential scoring of calcified lesions along the length of the abdominal aorta on DXA scans; mimicking the human scoring process.
dc.description.statementofresponsibilitySyed Zulqarnain Gilani, B, Naeha Sharif, David Suter, John T. Schousboe, Siobhan Reid, William D. Leslie, and Joshua R. Lewis
dc.identifier.citationLecture Notes in Artificial Intelligence, 2022 / Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (ed./s), vol.13433, pp.439-450
dc.identifier.doi10.1007/978-3-031-16437-8_42
dc.identifier.isbn9783031164361
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.orcidSuter, D. [0000-0001-6306-3023]
dc.identifier.urihttps://hdl.handle.net/2440/139822
dc.language.isoen
dc.publisherSpringer
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1183570
dc.relation.ispartofseriesLecture Notes in Computer Science; 13433
dc.rights© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
dc.source.urihttps://link.springer.com/book/10.1007/978-3-031-16437-8
dc.subjectAbdominal Aortic Calcification;Sequential prediction; Dual-energy xray
dc.titleShow, Attend and Detect: Towards Fine-Grained Assessment of Abdominal Aortic Calcification on Vertebral Fracture Assessment Scans
dc.typeConference paper
pubs.publication-statusPublished

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