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...
详细信息
ISBN:
(纸本)9781538636411
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 cines 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 21) LV segmentations of one short-axis and two long-axis images. By only employing these three 2dsegmentations, 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 +/- 0.15 and outperformed competing architectures (TL-net, dice score = 82.60 +/- 0.23, p = 2.2 . 10(-16)).
暂无评论