A spatio-temporal latent atlas for semi-supervised learning of fetal brain segmentations and morphological age estimation.
Prenatal neuroimaging requires reference models that reflect the normal spectrum of fetal brain development, and summarize observations from a representative sample of individuals. Collecting a sufficiently large data set of manually annotated data to construct a comprehensive in vivo atlas of rapidly developing structures is challenging but necessary for large population studies and clinical application. We propose a method for the semi-supervised learning of a spatio-temporal latent atlas of fetal brain development, and corresponding segmentations of emerging cerebral structures, such as the ventricles or cortex. The atlas is based on the annotation of a few examples, and a large number of imaging data without annotation. It models the morphological and developmental variability across the population. Furthermore, it serves as basis for the estimation of a structures' morphological age, and its deviation from the nominal gestational age during the assessment of pathologies. Experimental results covering the gestational period of 20-30 gestational weeks demonstrate segmentation accuracy achievable with minimal annotation, and precision of morphological age estimation. Age estimation results on fetuses suffering from lissencephaly demonstrate that they detect significant differences in the age offset compared to a control group.
Computational Imaging Research Lab (CIR), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria; Machine Vision Group, Profactor GmbH, Steyr, Austria. Electronic address: eva.dittrich@meduniwien.ac.at.
Eva
E
Dittrich
Tammy
T
Riklin Raviv
Gregor
G
Kasprian
René
R
Donner
Peter C
PC
Brugger
Daniela
D
Prayer
Georg
G
Langs
D000375:Aging; D000465:Algorithms; D000716:Anatomy, Artistic; D001185:Artificial Intelligence; D001271:Atlases as Topic; D001921:Brain / Q000033:anatomy & histology* / Q000196:embryology*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008953:Models, Anatomic; D008959:Models, Neurological; D011296:Prenatal Diagnosis / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D062211:Spatio-Temporal Analysis; D013382:Subtraction Technique
Fetal brain development; Magnetic resonance imaging; Segmentation; Spatio-temporal latent atlas