Learning a spatio-temporal latent atlas for fetal brain segmentation

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Fetal Magnetic Resonance Imaging (MRI) in early phases of
the cerebral development during gestation offers insights into the emergence of brain structures, their characteristics and variability across the
population. To collect substantial bodies of observations automatic analysis of these data is necessary. However, automatic segmentation proofs
challenging due to image quality, low contrast between brain tissues,
and the rapid development at this early age. Current atlas-based segmentation approaches perform well in the adult population, but they
are unable to cover the rapid changes during early development phases.
In this paper, we introduce a spatio-temporal group-wise segmentation
of fetal brain structures given a single annotated example. The method is
based on an emerging spatio-temporal latent atlas that captures the agedependent characteristics in the training population, and supports the
segmentation of brain structures. The proposed atlas makes segmentation of subcortical structures possible by integrating information across
a large number of subjects. It encodes the average development and its
variability, which is ultimately relevant for diagnosis. Furthermore, we
introduce a method to re-estimate each subject’s age to accommodate
variability in developmental speed. Results on 33 cases from 20th to
30th gestational week demonstrate improved segmentation results, and
an estimate of average development.
Original languageEnglish GB
Title of host publicationProceedings of the MICCAI 2011 Workshop on Image Analysis of Human Brain Development (IAHBD 2011)
StatePublished - 2011

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