Rabbani, A., Gao, H. , Lazarus, A., Dalton, D., Ge, Y., Mangion, K., Berry, C. and Husmeier, D. (2023) Image-based estimation of the left ventricular cavity volume using deep learning and Gaussian process with cardio-mechanical applications. Computerized Medical Imaging and Graphics, 106, 102203. (doi: 10.1016/j.compmedimag.2023.102203) (PMID:36848766)
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Abstract
In this investigation, an image-based method has been developed to estimate the volume of the left ventricular cavity using cardiac magnetic resonance (CMR) imaging data. Deep learning and Gaussian processes have been applied to bring the estimations closer to the cavity volumes manually extracted. CMR data from 339 patients and healthy volunteers have been used to train a stepwise regression model that can estimate the volume of the left ventricular cavity at the beginning and end of diastole. We have decreased the root mean square error (RMSE) of cavity volume estimation approximately from 13 to 8 ml compared to the common practice in the literature. Considering the RMSE of manual measurements is approximately 4 ml on the same dataset, 8 ml of error is notable for a fully automated estimation method, which needs no supervision or user-hours once it has been trained. Additionally, to demonstrate a clinically relevant application of automatically estimated volumes, we inferred the passive material properties of the myocardium given the volume estimates using a well-validated cardiac model. These material properties can be further used for patient treatment planning and diagnosis.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Berry, Professor Colin and Dalton, David and Rabbani, Dr Arash and Gao, Dr Hao and Lazarus, Dr Alan and Ge, Yuzhang and Mangion, Dr Kenneth and Husmeier, Professor Dirk |
| Authors: | Rabbani, A., Gao, H., Lazarus, A., Dalton, D., Ge, Y., Mangion, K., Berry, C., and Husmeier, D. |
| College/School: | College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health College of Science and Engineering > School of Mathematics and Statistics College of Science and Engineering > School of Mathematics and Statistics > Statistics |
| Journal Name: | Computerized Medical Imaging and Graphics |
| Publisher: | Elsevier |
| ISSN: | 0895-6111 |
| ISSN (Online): | 1879-0771 |
| Published Online: | 24 February 2023 |
| Copyright Holders: | Copyright © 2023 The Authors |
| First Published: | First published in Computerized Medical Imaging and Graphics 106: 102203 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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