A spatio-temporal latent atlas for semi-supervised learning of fetal brain segmentations and morphological age estimation

Eva Dittrich, Tamar Riklin Raviv, Gregor Kasprian, René Donner, Peter C. Brugger, Daniela Prayer, Georg Langs

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)9-21
Number of pages13
JournalMedical Image Analysis
Volume18
Issue number1
DOIs
StatePublished - 1 Jan 2014
Externally publishedYes

Keywords

  • Fetal brain development
  • Magnetic resonance imaging
  • Segmentation
  • Spatio-temporal latent atlas

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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