3D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoders
| Autor: | Biffi, Carlo, Cerrolaza, Juan J., Tarroni, Giacomo, de Marvao, Antonio, Cook, Stuart A., O'Regan, Declan P., Rueckert, Daniel |
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| Rok vydání: | 2019 |
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| Druh dokumentu: | Working Paper |
| Popis: | Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology. High-resolution 3D MR sequences enable whole-heart structural imaging but are time-consuming, expensive to acquire and they often require long breath holds that are not suitable for patients. Consequently, multiplanar breath-hold 2D cine sequences are standard practice but are disadvantaged by lack of whole-heart coverage and low through-plane resolution. To address this, we propose a conditional variational autoencoder architecture able to learn a generative model of 3D high-resolution left ventricular (LV) segmentations which is conditioned on three 2D LV segmentations of one short-axis and two long-axis images. By only employing these three 2D segmentations, our model can efficiently reconstruct the 3D high-resolution LV segmentation of a subject. When evaluated on 400 unseen healthy volunteers, our model yielded an average Dice score of $87.92 \pm 0.15$ and outperformed competing architectures. Comment: Accepted in IEEE International Symposium on Biomedical Imaging (ISBI 2019) |
| Databáze: | arXiv |
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