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.
S. Z. Gilani and N. Sharif—Joint First Authors.
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Acknowledgement
The work was funded by a National Health and Medical Research Council (NH &MRC) of Australia Ideas grants (APP1183570). The salary of JRL is supported by a National Heart Foundation of Australia Future Leader Fellowship (ID: 102817). The study was approved by the Health Research Ethics Board for the University of Manitoba (HREB H2004:017L, HS20121). The Manitoba Health Information Privacy Committee approved access to the Manitoba data (HIPC 2016/2017-29). The results and conclusions are those of the authors and no official endorsement by Manitoba Health and Seniors Care, or other data providers is intended or should be inferred.
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Gilani, S.Z. et al. (2022). Show, Attend and Detect: Towards Fine-Grained Assessment of Abdominal Aortic Calcification on Vertebral Fracture Assessment Scans. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13433. Springer, Cham. https://doi.org/10.1007/978-3-031-16437-8_42
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