Quantitative digital pathology enables automated and quantitative assessment of inflammatory activity in patients with autoimmune hepatitis.

Background: Chronic liver disease diagnoses depend on liver biopsy histopathological assessment. However, due to the limitations associated with biopsy, there is growing interest in the use of quantitative digital pathology to support pathologists. We evaluated the performance of computational algorithms in the assessment of hepatic inflammation in an autoimmune hepatitis in which inflammation is a major component.

Estimation of field inhomogeneity map following magnitude-based ambiguity-resolved water-fat separation.

Magnitude-based PDFF (Proton Density Fat Fraction) and R mapping with resolved water-fat ambiguity is extended to calculate field inhomogeneity (field map) using the phase images. The estimation is formulated in matrix form, resolving the field map in a least-squares sense. PDFF and R from magnitude fitting may be updated using the estimated field maps.

Pancreas MRI Segmentation Into Head, Body, and Tail Enables Regional Quantitative Analysis of Heterogeneous Disease.

Background: Quantitative imaging studies of the pancreas have often targeted the three main anatomical segments, head, body, and tail, using manual region of interest strategies to assess geographic heterogeneity. Existing automated analyses have implemented whole-organ segmentation, providing overall quantification but failing to address spatial heterogeneity.

Purpose: To develop and validate an automated method for pancreas segmentation into head, body, and tail subregions in abdominal MRI.

Modelling brain development to detect white matter injury in term and preterm born neonates.

Premature birth occurs during a period of rapid brain growth. In this context, interpreting clinical neuroimaging can be complicated by the typical changes in brain contrast, size and gyrification occurring in the background to any pathology. To model and describe this evolving background in brain shape and contrast, we used a Bayesian regression technique, Gaussian process regression, adapted to multiple correlated outputs.

Decreased microglial Wnt/β-catenin signalling drives microglial pro-inflammatory activation in the developing brain.

Microglia of the developing brain have unique functional properties but how their activation states are regulated is poorly understood. Inflammatory activation of microglia in the still-developing brain of preterm-born infants is associated with permanent neurological sequelae in 9 million infants every year. Investigating the regulators of microglial activation in the developing brain across models of neuroinflammation-mediated injury (mouse, zebrafish) and primary human and mouse microglia we found using analysis of genes and proteins that a reduction in Wnt/β-catenin signalling is necessary and sufficient to drive a microglial phenotype causing hypomyelination.