3D black blood cardiovascular magnetic resonance atlases of congenital aortic arch anomalies and the normal fetal heart: application to automated multi-label segmentation

Abstract

Background Image-domain motion correction of black-blood contrast T2-weighted fetal cardiovascular magnetic resonance imaging (CMR) using slice-to-volume registration (SVR) provides high-resolution three-dimensional (3D) images of the fetal heart providing excellent 3D visualisation of vascular anomalies [1]. However, 3D segmentation of these datasets, important for both clinical reporting and the application of advanced analysis techniques is currently a time-consuming process requiring manual input with potential for inter-user variability. Methods In this work, we present novel 3D fetal CMR population-averaged atlases of normal and abnormal fetal cardiovascular anatomy. The atlases are created using motion-corrected 3D reconstructed volumes of 86 third trimester fetuses (gestational age range 29-34 weeks) including: 28 healthy controls, 20 cases with postnatally confirmed neonatal coarctation of the aorta (CoA) and 38 vascular rings (21 right aortic arch (RAA), 17 double aortic arch (DAA)). We used only high image quality datasets with isolated anomalies and without any other deviations in the cardiovascular anatomy.In addition, we implemented and evaluated atlas-guided registration and deep learning (UNETR) methods for automated 3D multi-label segmentation of fetal cardiac vessels. We used images from CoA, RAA and DAA cohorts including: 42 cases for training (14 from each cohort), 3 for validation and 6 for testing. In addition, the potential limitations of the network were investigated on unseen datasets including 3 early gestational age (22 weeks) and 3 low SNR cases. Results We created four atlases representing the average anatomy of the normal fetal heart, postnatally confirmed neonatal CoA, RAA and DAA. Visual inspection was undertaken to verify expected anatomy per subgroup. The results of the multi-label cardiac vessel UNETR segmentation showed 100$$\%$$ % per-vessel detection rate for both normal and abnormal aortic arch anatomy. Conclusions This work introduces the first set of 3D black-blood T2-weighted CMR atlases of normal and abnormal fetal cardiovascular anatomy including detailed segmentation of the major cardiovascular structures. Additionally, we demonstrated the general feasibility of using deep learning for multi-label vessel segmentation of 3D fetal CMR images.

Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI

“…A key novelty is the application of our framework to fetal Cardiac Magnetic Resonance (CMR) data, which is to date largely unexplored. Our framework expands on our previous work (Uus et al, 2022b), which for the first time proposed an automated segmentation of fetal cardiac vessels by training a CNN on labels propagated from anomaly-specific atlases.…”

“…2.2). In order to achieve our multi-class output, we employ three fully-labelled atlases 1 (Uus et al (2022b), see Fig. 3b), one per anomaly (RAA, DAA and CoA).…”

“…Our benchmark framework is based on Ramirez Gilliland et al (2022), where Attention U-Net (Oktay et al, 2018) is trained using both manual binary labels and multi-class labels propagated from an atlas (Uus et al, 2022b).…”

Automated body organ segmentation, volumetry and population-averaged atlas for 3D motion-corrected T2-weighted fetal body MRI

“…Recently, there have been several studies that have used automated 3D parcellation for the fetal body organs 9 , 16 , 17 based on atlas label propagation and deep learning for segmentation of the lungs and heart vessels. Yet, despite the successes in the application of deep learning for multi-label segmentation of the fetal brain 18 , to our knowledge, there has been no reported dedicated automated methods for simultaneous segmentation of multiple fetal body organs such as liver, spleen, thymus or kidneys.…”

Structurally Optimized Fluorinated Small‐Sized Graphene Oxide for Enhanced Magnetic Resonance Imaging during Pregnancy

“…The utilization of magnetic resonance imaging (MRI) in clinical diagnosis has become increasingly prevalent among pregnant women in recent years due to its capacity to accurately assess fetal status and investigate various maternal benign and malignant conditions. [ 1‐5 ] With the trend of incremental prevalence and mortality of gynecological malignancies during pregnancy worldwide, particularly endometrial, cervical, and ovarian cancers, there is an increasing demand for various contrast agents (CAs) to more comprehensively identify the imaging and qualitative characteristics of tumor lesions. [ 6‐8 ] It has been demonstrated that gadolinium‐based complex contrast agents (GBCAs), which are frequently utilized, readily traverse the placental barrier, enter the fetal circulation, and manifest in the fetal bladder within 11 min of intravenous administration.…”