ABSTRACT
Zika virus (ZIKV) is a flavivirus with teratogenic effects on fetal brain, but the spectrum of ZIKV-induced brain injury is unknown, particularly when ultrasound imaging is normal. In a pregnant pigtail macaque (Macaca nemestrina) model of ZIKV infection, we demonstrate that ZIKV-induced injury to fetal brain is substantial, even in the absence of microcephaly, and may be challenging to detect in a clinical setting. A common and subtle injury pattern was identified, including (i) periventricular T2-hyperintense foci and loss of fetal noncortical brain volume, (ii) injury to the ependymal epithelium with underlying gliosis and (iii) loss of late fetal neuronal progenitor cells in the subventricular zone (temporal cortex) and subgranular zone (dentate gyrus, hippocampus) with dysmorphic granule neuron patterning. Attenuation of fetal neurogenic output demonstrates potentially considerable teratogenic effects of congenital ZIKV infection even without microcephaly. Our findings suggest that all children exposed to ZIKV in utero should receive long-term monitoring for neurocognitive deficits, regardless of head size at birth.
Subject(s)
Fetus/virology , Pregnancy Complications, Infectious/physiopathology , Zika Virus Infection/virology , Zika Virus/pathogenicity , Animals , Disease Models, Animal , Female , Fetus/physiopathology , Humans , Macaca nemestrina/virology , Microcephaly/diagnostic imaging , Microcephaly/physiopathology , Microcephaly/virology , Neurogenesis/genetics , Pregnancy , Pregnancy Complications, Infectious/diagnostic imaging , Pregnancy Complications, Infectious/virology , Zika Virus/genetics , Zika Virus Infection/genetics , Zika Virus Infection/physiopathologyABSTRACT
This paper presents a variational level set approach to joint segmentation and bias correction of images with intensity inhomogeneity. Our method is based on an observation that intensities in a relatively small local region are separable, despite of the inseparability of the intensities in the whole image caused by the intensity inhomogeneity. We first define a weighted K-means clustering objective function for image intensities in a neighborhood around each point, with the cluster centers having a multiplicative factor that estimates the bias within the neighborhood. The objective function is then integrated over the entire domain and incorporated into a variational level set formulation. The energy minimization is performed via a level set evolution process. Our method is able to estimate bias of quite general profiles. Moreover, it is robust to initialization, and therefore allows automatic applications. The proposed method has been used for images of various modalities with promising results.
