Modeling fetal cortical expansion using graph-regularized Gompertz models

Abstract

Understanding patterns of brain development before birth is of both high clinical and scientific interest. However,despite advances in reconstruction methods,the challenging setting of in-utero imaging renders precise,point-wise measurements of the rapidly changing fetal brain morphology difficult. This paper proposes a method to deal with bad measurement quality due to image noise,motion artefacts and ensuing segmentation and registration errors by enforcing spatial regularity during the estimation of parametric models of cortical expansion. Qualitative and quantitative analysis of the proposed method was performed on 88 clinical fetal MR volumes.We show that the resulting models accurately capture the morphological and temporal properties of fetal brain development by predicting gestational age on unseen cases at humanlevel accuracy.