Generalizable fully automated multi-label segmentation of four-chamber view echocardiograms based on deep convolutional adversarial networks
| Autor: | Hamid Jafarkhani, Arghavan Arafati, Daisuke Morisawa, M Reza Amini, Arash Kheradvar, Ramin Assadi, M. R. Avendi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Generalization Biomedical Engineering Biophysics Bioengineering 030204 cardiovascular system & hematology Translation (geometry) Biochemistry 030218 nuclear medicine & medical imaging Machine Learning Biomaterials 03 medical and health sciences 0302 clinical medicine Artificial Intelligence Image Processing Computer-Assisted Humans Segmentation Generalizability theory Life Sciences–Engineering interface Cardiac imaging business.industry Deep learning Volume (computing) Heart Pattern recognition Metric (mathematics) Neural Networks Computer Artificial intelligence business Biotechnology |
| Zdroj: | J R Soc Interface |
| ISSN: | 1742-5662 1742-5689 |
| DOI: | 10.1098/rsif.2020.0267 |
| Popis: | A major issue in translation of the artificial intelligence platforms for automatic segmentation of echocardiograms to clinics is their generalizability. The present study introduces and verifies a novel generalizable and efficient fully automatic multi-label segmentation method for four-chamber view echocardiograms based on deep fully convolutional networks (FCNs) and adversarial training. For the first time, we used generative adversarial networks for pixel classification training, a novel method in machine learning not currently used for cardiac imaging, to overcome the generalization problem. The method's performance was validated against manual segmentations as the ground-truth. Furthermore, to verify our method's generalizability in comparison with other existing techniques, we compared our method's performance with a state-of-the-art method on our dataset in addition to an independent dataset of 450 patients from the CAMUS (cardiac acquisitions for multi-structure ultrasound segmentation) challenge. On our test dataset, automatic segmentation of all four chambers achieved a dice metric of 92.1%, 86.3%, 89.6% and 91.4% for LV, RV, LA and RA, respectively. LV volumes' correlation between automatic and manual segmentation were 0.94 and 0.93 for end-diastolic volume and end-systolic volume, respectively. Excellent agreement with chambers’ reference contours and significant improvement over previous FCN-based methods suggest that generative adversarial networks for pixel classification training can effectively design generalizable fully automatic FCN-based networks for four-chamber segmentation of echocardiograms even with limited number of training data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|