Automated segmentation of human cardiac magnetic resonance datasets has been steadily improving during recent years. Similar applications would be highly useful to improve and speed up the studies of cardiac function in rodents in the preclinical context. However, the transfer of such segmentation methods to the preclinical research is compounded by the limited number of datasets and lower image resolution. In this paper we present a successful application of deep architectures 3D cardiac segmentation for rats in preclinical contexts which to our knowledge has not yet been reported. We developed segmentation models that expand on the standard U-Net architecture and evaluated models separately trained for systole and diastole phases (2MSA) and a single model trained for all phases (1MSA). Furthermore, we calibrated model outputs using a Gaussian process (GP)-based prior to improve phase selection. The resulting models approach human performance in terms of left ventricular segmentation quality and ejection fraction (EF) estimation in both 1MSA and 2MSA settings (Sørensen-Dice score 0.91 ± 0.072 and 0.93 ± 0.032, respectively). 2MSA achieved a mean absolute difference between estimated and reference EF of 3.5 ± 2.5%, while 1MSA resulted in 4.1 ± 3.0%. Applying GPs to 1MSA enabled automating systole and diastole phase selection. Both segmentation approaches (1MSA and 2MSA) were statistically equivalent. Combined with a proposed cardiac phase selection strategy, our work presents an important first step towards a fully automated segmentation pipeline in the context of rat cardiac analysis.
In translational cardiovascular research, delineation of left ventricle (LV) in magnetic resonance images is a crucial step in assessing heart's function. Performed manually, this task is time-consuming and prone to inter- and intra-reader variability. Here we report first AI-based tool for segmentation of rat cardiovascular MRI. The method is an ensemble of fully convolutional networks and can quantify clinically relevant measures: end-diastolic volume (EDV), end-systolic volume (ESV), and ejection fraction (EF) automatically. Overall, our method reaches Dice score of 0.93 on the independent test set. The mean absolute difference of segmented volumes between automated and manual segmentation is 22.5μL for EDV, 13.6μL for ESV, and for EF 2.9%. Our work demonstrates the value of AI in development of tools that will significantly reduce time spent on repetitive work and result in increased efficiency of reporting data to project teams.
The sensorimotor (SM) network is crucial for optimal neurodevelopment. However, undergoing rapid maturation during the perinatal period, it is particularly vulnerable to preterm birth. Our work explores the prematurity impact on the microstructure and maturation of primary SM white matter (WM) tracts at term-equivalent age (TEA) and evaluates the relationships between these alterations and neurodevelopmental outcome. We analyzed diffusion MRI data from the developing Human Connectome Project (dHCP) database: 59 preterm (PT) low-risk infants scanned near TEA, compared to a control group of full-term (FT) neonates paired for age at MRI and sex. We dissected pairwise connections between primary SM cortices and subcortical structures using probabilistic tractography and evaluated their microstructure with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. In addition to tract-specific univariate analyses of diffusion metrics, we computed a maturational distance related to prematurity based on a multi-parametric Mahalanobis distance of each PT infant relative to the FT group. Finally, we evaluated the relationships between this distance and Bayley Scales of Infant and Toddler Development (BSID-III) scaled scores at 18 months corrected age. Our results confirm important microstructural differences in SM tracts between PT and FT infants, with effects increasing with lower gestational age at birth. Additionally, comparisons of maturational distances highlight that prematurity has a differential effect on SM tracts which follows the established WM caudo-rostral developmental pattern. Our results suggest a particular vulnerability of projections involving the primary sensorimotor cortices (S1) and of the most rostral tracts, with cortico-cortical and S1-Lenticular tracts presenting the highest alterations at TEA. Finally, NODDI-derived maturational distances of specific tracts seem related to fine motor and cognitive scores. This study expands the understanding of the impact of early WM alterations in the emerging SM network on long-term neurodevelopment. In the future, related approaches have potential to lead to the development of neuroimaging markers for neurodevelopmental disorders, with special interest for subtle neuromotor impairments frequently observed in preterm-born children.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.