English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper describes eort towards automatic tissue segmen- tation in neonatal MRI. Extremely low contrast to noise ratio (CNR), regional intensity changes due to RF coil inhomogeneity and biology, and tissue property changes due to the early myelination and axon pruning processes require a methodology that combines the strength of spatial priors (template atlas), data modelling, and prior knowledge about brain development. We use an EM-type algorithm that includes tissue classi- fication, inhomogeneity correction and brain stripping into an iterative optimization scheme using a mixture distribution model. A statistical brain atlas registered to the subject image serves as a spatial prior. White matter in neonates is modeled as a mixture model of non-myelinated and myelinated regions. A pilot study on 10 neonates demonstrates the feasibility of high-resolution neonatal MRI and of automatic tissue seg- mentation. Results demonstrate that interleaved segmentation and inho- mogeneity correction, guided by a statistical spatial prior, will provide a powerful and ecient segmentation framework for this type of imaging data. It is demonstrated that the mixture model for white matter allows us to segment early myelination regions of the projection tract up to the motor cortex, while also providing non-myelinated white, gray and csf segmentation. The early myelination regions are hypothesized to develop early but have not yet been shown in quantitative MRI studies. Neonatal brain MRI segmentation is very challenging due to low contrast, tis- sue intensity variation, scanner bias field, lack of contrast, and fractional myeli- nation of white matter. Results presented in (1) clearly demonstrate the diculty as brain segmentation of 1 months old infant MRI mostly show noise although visual inspection of the MRI leaves the impression that there are discernable in- tensity dierences for non-myelinated white and for gray matter. Most advanced

Assessing Early Brain Development in Neonates by Segmentation of High-Resolution 3T MRI