Title

Show, attend and detect: Towards fine-grained assessment of abdominal aortic calcification on vertebral fracture assessment scans

Document Type

Conference Proceeding

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

13433 LNCS

First Page

439

Last Page

450

Publisher

Springer

School

Nutrition and Health Innovation Research Institute / Centre for Artificial Intelligence and Machine Learning (CAIML) / School of Science

RAS ID

46965

Funders

National Health and Medical Research Council

Grant Number

NHMRC Number : APP1183570

Comments

Gilani, S. Z., Sharif, N., Suter, D., Schousboe, J. T., Reid, S., Leslie, W. D., & Lewis, J. R. (2022). Show, attend and detect: Towards fine-grained assessment of abdominal aortic calcification on vertebral fracture assessment scans. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 439-450). Springer, Cham. Advance online publication. https://doi.org/10.1007/978-3-031-16437-8_42

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 “fine-grained” 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.

DOI

10.1007/978-3-031-16437-8_42

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