Fully‑automated deep‑learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseases
| Autor: | Arash Kheradvar, Saeed Karimi-Bidhendi, Andrew L. Cheng, Yilei Wu, Hamid Jafarkhani, Arghavan Arafati |
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| Rok vydání: | 2020 |
| Předmět: |
Male
lcsh:Diseases of the circulatory (Cardiovascular) system Generative adversarial networks Statistical difference 030204 cardiovascular system & hematology Cardiorespiratory Medicine and Haematology Cardiovascular Accurate segmentation 030218 nuclear medicine & medical imaging Workflow Congenital Automation Computer-Assisted 0302 clinical medicine Segmentation Fully convolutional networks Child Heart Defects Pediatric screening and diagnosis Radiological and Ultrasound Technology medicine.diagnostic_test Age Factors Magnetic Resonance Imaging Detection Nuclear Medicine & Medical Imaging Heart Disease Fully automated Child Preschool Metric (mathematics) Biomedical Imaging Female Cardiology and Cardiovascular Medicine Complex CHD analysis 4.2 Evaluation of markers and technologies Heart Defects Congenital Adolescent Bioengineering 03 medical and health sciences Predictive Value of Tests Image Interpretation Computer-Assisted Machine learning medicine Humans Radiology Nuclear Medicine and imaging Preschool Image Interpretation business.industry Deep learning Research Reproducibility of Results Magnetic resonance imaging Pattern recognition CMR image analysis Hausdorff distance lcsh:RC666-701 Artificial intelligence business |
| Zdroj: | Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance, vol 22, iss 1 Journal of Cardiovascular Magnetic Resonance Journal of Cardiovascular Magnetic Resonance, Vol 22, Iss 1, Pp 1-24 (2020) |
| Popis: | Background For the growing patient population with congenital heart disease (CHD), improving clinical workflow, accuracy of diagnosis, and efficiency of analyses are considered unmet clinical needs. Cardiovascular magnetic resonance (CMR) imaging offers non-invasive and non-ionizing assessment of CHD patients. However, although CMR data facilitates reliable analysis of cardiac function and anatomy, clinical workflow mostly relies on manual analysis of CMR images, which is time consuming. Thus, an automated and accurate segmentation platform exclusively dedicated to pediatric CMR images can significantly improve the clinical workflow, as the present work aims to establish. Methods Training artificial intelligence (AI) algorithms for CMR analysis requires large annotated datasets, which are not readily available for pediatric subjects and particularly in CHD patients. To mitigate this issue, we devised a novel method that uses a generative adversarial network (GAN) to synthetically augment the training dataset via generating synthetic CMR images and their corresponding chamber segmentations. In addition, we trained and validated a deep fully convolutional network (FCN) on a dataset, consisting of $$64$$ 64 pediatric subjects with complex CHD, which we made publicly available. Dice metric, Jaccard index and Hausdorff distance as well as clinically-relevant volumetric indices are reported to assess and compare our platform with other algorithms including U-Net and cvi42, which is used in clinics. Results For congenital CMR dataset, our FCN model yields an average Dice metric of $$91.0\mathrm{\%}$$ 91.0 % and $$86.8\mathrm{\%}$$ 86.8 % for LV at end-diastole and end-systole, respectively, and $$84.7\mathrm{\%}$$ 84.7 % and $$80.6\mathrm{\%}$$ 80.6 % for RV at end-diastole and end-systole, respectively. Using the same dataset, the cvi42, resulted in $$73.2\mathrm{\%}$$ 73.2 % , $$71.0\mathrm{\%}$$ 71.0 % , $$54.3\mathrm{\%}$$ 54.3 % and $$53.7\mathrm{\%}$$ 53.7 % for LV and RV at end-diastole and end-systole, and the U-Net architecture resulted in $$87.4\mathrm{\%}$$ 87.4 % , $$83.9\mathrm{\%}$$ 83.9 % , $$81.8\mathrm{\%}$$ 81.8 % and $$74.8\mathrm{\%}$$ 74.8 % for LV and RV at end-diastole and end-systole, respectively. Conclusions The chambers’ segmentation results from our fully-automated method showed strong agreement with manual segmentation and no significant statistical difference was found by two independent statistical analyses. Whereas cvi42 and U-Net segmentation results failed to pass the t-test. Relying on these outcomes, it can be inferred that by taking advantage of GANs, our method is clinically relevant and can be used for pediatric and congenital CMR segmentation and analysis. |
| Databáze: | OpenAIRE |
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