分割
计算机科学
主动脉弓
胎儿超声心动图
人工智能
双主动脉弓
伟大的船只
主动脉
心脏病
模式识别(心理学)
医学
胎儿
产前诊断
心脏病学
怀孕
生物
遗传学
作者
Paula Ramirez Gilliland,Alena Uus,Milou P. M. van Poppel,Irina Grigorescu,Johannes K. Steinweg,David Lloyd,Kuberan Pushparajah,Andrew P. King,Maria Deprez
标识
DOI:10.1007/978-3-031-17117-8_8
摘要
Congenital heart disease (CHD) encompasses a range of cardiac malformations present from birth, representing the leading congenital diagnosis. 3D volumetric reconstructions of T2w black blood fetal MRI provide optimal vessel visualisation, supporting prenatal CHD diagnosis, a key step for optimal patient management. We present a framework for automated multi-class fetal vessel segmentation in the setting where binary manual labels of the vessels region of interest (ROI) are available for training, as well as a multi-class labelled atlas. We combine deep learning label propagation from multi-class labelled condition-specific atlases with 3D Attention U-Net segmentation to achieve the desired multi-class output. We train a single network to segment 12 fetal cardiac vessels for three distinct aortic arch anomalies (double aortic arch, right aortic arch, and suspected coarctation of the aorta). Our segmentation network is trained by combination of a multi-class loss, which uses the propagated multi-class labels; and a binary loss which uses binary labels generated by expert clinicians. Our proposed method outperforms label propagation in accuracy of vessel segmentation, while succeeding in segmenting the anomaly area of all three CHD diagnoses included, achieving a 100% vessel detection rate.
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