Conference item
Going deeper into cardiac motion analysis to model fine spatio-temporal features
- Abstract:
- This paper shows that deep modelling of subtle changes of cardiac motion can help in automated diagnosis of early onset of cardiac disease. In this paper, we model left ventricular (LV) cardiac motion in MRI sequences, based on a hybrid spatio-temporal network. Temporal data over long time periods is used as inputs to the model and delivers a dense displacement field (DDF) for regional analysis of LV function. A segmentation mask of the end-diastole (ED) frame is deformed by the predicted DDF from which regional analysis of LV function endocardial radius, thickness, circumferential strain (Ecc) and radial strain (Err) are estimated. Cardiac motion is estimated over MR cine loops. We compare the proposed technique to two other deep learning-based approaches and show that the proposed approach achieves promising predicted DDFs. Predicted DDFs are estimated on imaging data from healthy volunteers and patients with primary pulmonary hypertension from the UK Biobank. Experiments demonstrate that the proposed methods perform well in obtaining estimates of endocardial radii as cardiac motion-characteristic features for regional LV analysis.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, 2.0MB, Terms of use)
-
- Publisher copy:
- 10.1007/978-3-030-52791-4_23
Authors
Contributors
+ Papiez, BW
- Role:
- Editor
+ Namburete, AIL
- Role:
- Editor
+ Yaqub, M
- Role:
- Editor
+ Noble, JA
- Role:
- Editor
- Publisher:
- Springer
- Host title:
- MIUA 2020: Medical Image Understanding and Analysis
- Pages:
- 294-306
- Series:
- Communications in Computer and Information Science
- Series number:
- 1248
- Publication date:
- 2020-07-08
- Acceptance date:
- 2020-04-30
- Event title:
- MIUA 2020: Medical Image Understanding and Analysis
- Event series:
- Annual Conference of Medical Image Understanding and Analysis
- Event location:
- Online
- Event website:
- https://miua2020.com/
- Event start date:
- 2020-07-15
- Event end date:
- 2020-07-17
- DOI:
- ISSN:
-
1865-0929
- EISBN:
- 978-3-030-52791-4
- ISBN:
- 978-3-030-52790-7
- Language:
-
English
- Keywords:
- Pubs id:
-
1125834
- Local pid:
-
pubs:1125834
- Deposit date:
-
2020-11-13
Terms of use
- Copyright holder:
- Springer Nature Switzerland AG
- Copyright date:
- 2020
- Rights statement:
- © Springer Nature Switzerland AG 2020
- Notes:
- This is the accepted manuscript version of the article. The final version is available online from Springer Nature at https://doi.org/10.1007/978-3-030-52791-4_23
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