Reduced and stable feature sets selection with random forest for neurons segmentation in histological images of macaque brain.

In preclinical research, histology images are produced using powerful optical microscopes to digitize entire sections at cell scale. Quantification of stained tissue relies on machine learning driven segmentation. However, such methods require multiple additional information, or features, which are increasing the quantity of data to process. As a result, the quantity of features to deal with represents a drawback to process large series or massive histological images rapidly in a robust manner. Existing feature selection methods can reduce the amount of required information but the selected subsets lack reproducibility. We propose a novel methodology operating on high performance computing (HPC) infrastructures and aiming at finding small and stable sets of features for fast and robust segmentation of high-resolution histological images. This selection has two steps: (1) selection at features families scale (an intermediate pool of features, between spaces and individual features) and (2) feature selection performed on pre-selected features families. We show that the selected sets of features are stables for two different neuron staining. In order to test different configurations, one of these dataset is a mono-subject dataset and the other is a multi-subjects dataset to test different configurations. Furthermore, the feature selection results in a significant reduction of computation time and memory cost. This methodology will allow exhaustive histological studies at a high-resolution scale on HPC infrastructures for both preclinical and clinical research.

Assessment of MRI-Based Automated Fetal Cerebral Cortical Folding Measures in Prediction of Gestational Age in the Third Trimester.

BACKGROUND AND PURPOSE: Traditional methods of dating a pregnancy based on history or sonographic assessment have a large variation in the third trimester. We aimed to assess the ability of various quantitative measures of brain cortical folding on MR imaging in determining fetal gestational age in the third trimester. MATERIALS AND METHODS: We evaluated 8 different quantitative cortical folding measures to predict gestational age in 33 healthy fetuses by using T2-weighted fetal MR imaging. We compared the accuracy of the prediction of gestational age by these cortical folding measures with the accuracy of prediction by brain volume measurement and by a previously reported semiquantitative visual scale of brain maturity. Regression models were constructed, and measurement biases and variances were determined via a cross-validation procedure. RESULTS: The cortical folding measures are accurate in the estimation and prediction of gestational age (mean of the absolute error, 0.43 ± 0.45 weeks) and perform better than (P = .024) brain volume (mean of the absolute error, 0.72 ± 0.61 weeks) or sonography measures (SDs approximately 1.5 weeks, as reported in literature). Prediction accuracy is comparable with that of the semiquantitative visual assessment score (mean, 0.57 ± 0.41 weeks). CONCLUSIONS: Quantitative cortical folding measures such as global average curvedness can be an accurate and reliable estimator of gestational age and brain maturity for healthy fetuses in the third trimester and have the potential to be an indicator of brain-growth delays for at-risk fetuses and preterm neonates.

Delayed cortical development in fetuses with complex congenital heart disease.

Neurologic impairment is a major complication of complex congenital heart disease (CHD). A growing body of evidence suggests that neurologic dysfunction may be present in a significant proportion of this high-risk population in the early newborn period prior to surgical interventions. We recently provided the first evidence that brain growth impairment in fetuses with complex CHD has its origins in utero. Here, we extend these observations by characterizing global and regional brain development in fetuses with hypoplastic left heart syndrome (HLHS), one of the most severe forms of CHD. Using advanced magnetic resonance imaging techniques, we compared in vivo brain growth in 18 fetuses with HLHS and 30 control fetuses from 25.4-37.0 weeks of gestation. Our findings demonstrate a progressive third trimester fall-off in cortical gray and white matter volumes (P < 0.001), and subcortical gray matter (P < 0.05) in fetuses with HLHS. Significant delays in cortical gyrification were also evident in HLHS fetuses (P < 0.001). In the HLHS fetus, local cortical folding delays were detected as early as 25 weeks in the frontal, parietal, calcarine, temporal, and collateral regions and appear to precede volumetric brain growth disturbances, which may be an early marker of elevated risk for third trimester brain growth failure.

Model-driven parameterization of the cortical surface for localization and inter-subject matching.

In this paper we present a generic and organized model of cortical folding, and a way to implement this model on any given cortical surface. This results in a model-driven parameterization, providing an anatomically meaningful coordinate system for cortical localization, and implicitly defining inter-subject surface matching without any deformation of surfaces. We present our cortical folding model and show how it naturally defines a parameterization of the cortex. The mapping of the model to any given cortical surface is detailed, leading to an anatomically invariant coordinate system. The process is evaluated on real data in terms of both anatomical and functional localization, and shows improved performance compared to a traditional volume-based normalization. It is fully automatic and available with the BrainVISA software platform.

Anatomically constrained surface parameterization for cortical localization.

We present here a method that aims at defining a surface-based coordinate system on the cortical surface. Such a system is needed for both cortical localization and intersubject matching in the framework of neuroimaging. We propose an automatic parameterization based on the spherical topology of the grey/white matter interface of each hemisphere and on the use of naturally organized and reproducible anatomical features. From those markers used as initial constraints, the coordinate system is propagated via a PDE solved on the cortical surface.