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https://hdl.handle.net/2440/139822
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Type: | Conference paper |
Title: | Show, Attend and Detect: Towards Fine-Grained Assessment of Abdominal Aortic Calcification on Vertebral Fracture Assessment Scans |
Author: | Gilani, S.Z. Sharif, N. Suter, D. Schousboe, J.T. Reid, S. Leslie, W.D. Lewis, J.R. |
Citation: | Lecture Notes in Artificial Intelligence, 2022 / Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (ed./s), vol.13433, pp.439-450 |
Publisher: | Springer |
Issue Date: | 2022 |
Series/Report no.: | Lecture Notes in Computer Science; 13433 |
ISBN: | 9783031164361 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (18 Sep 2022 - 22 Sep 2022 : Singapore) |
Editor: | Wang, L. Dou, Q. Fletcher, P.T. Speidel, S. Li, S. |
Statement of Responsibility: | Syed Zulqarnain Gilani, B, Naeha Sharif, David Suter, John T. Schousboe, Siobhan Reid, William D. Leslie, and Joshua R. Lewis |
Abstract: | More 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. |
Keywords: | Abdominal Aortic Calcification;Sequential prediction; Dual-energy xray |
Rights: | © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 |
DOI: | 10.1007/978-3-031-16437-8_42 |
Grant ID: | http://purl.org/au-research/grants/nhmrc/1183570 |
Published version: | https://link.springer.com/book/10.1007/978-3-031-16437-8 |
Appears in Collections: | Computer Science publications |
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