giRAff: an automated atlas segmentation tool adapted to single histological slices

Conventional histology of the brain remains the gold standard in the analysis of animal models. In most biological studies, standard protocols usually involve producing a limited number of histological slices to be analyzed. These slices are often selected into a specific anatomical region of interest or around a specific pathological lesion. Due to the lack of automated solutions to analyze such single slices, neurobiologists perform the segmentation of anatomical regions manually most of the time. Because the task is long, tedious, and operator-dependent, we propose an automated atlas segmentation method called giRAff, which combines rigid and affine registrations and is suitable for conventional histological protocols involving any number of single slices from a given mouse brain. In particular, the method has been tested on several routine experimental protocols involving different anatomical regions of different sizes and for several brains. For a given set of single slices, the method can automatically identify the corresponding slices in the mouse Allen atlas template with good accuracy and segmentations comparable to those of an expert. This versatile and generic method allows the segmentation of any single slice without additional anatomical context in about 1 min. Basically, our proposed giRAff method is an easy-to-use, rapid, and automated atlas segmentation tool compliant with a wide variety of standard histological protocols.

Mu-Net a Light Architecture for Small Dataset Segmentation of Brain Organoid Bright-Field Images

To characterize the growth of brain organoids (BOs), cultures that replicate some early physiological or pathological developments of the human brain are usually manually extracted. Due to their novelty, only small datasets of these images are available, but segmenting the organoid shape automatically with deep learning (DL) tools requires a larger number of images. Light U-Net segmentation architectures, which reduce the training time while increasing the sensitivity under small input datasets, have recently emerged. We further reduce the U-Net architecture and compare the proposed architecture (MU-Net) with U-Net and UNet-Mini on bright-field images of BOs using several data augmentation strategies. In each case, we perform leave-one-out cross-validation on 40 original and 40 synthesized images with an optimized adversarial autoencoder (AAE) or on 40 transformed images. The best results are achieved with U-Net segmentation trained on optimized augmentation. However, our novel method, MU-Net, is more robust: it achieves nearly as accurate segmentation results regardless of the dataset used for training (various AAEs or a transformation augmentation). In this study, we confirm that small datasets of BOs can be segmented with a light U-Net method almost as accurately as with the original method.

Brain organoid data synthesis and evaluation

Introduction

Datasets containing only few images are common in the biomedical field. This poses a global challenge for the development of robust deep-learning analysis tools, which require a large number of images. Generative Adversarial Networks (GANs) are an increasingly used solution to expand small datasets, specifically in the biomedical domain. However, the validation of synthetic images by metrics is still controversial and psychovisual evaluations are time consuming.

Methods

We augment a small brain organoid bright-field database of 40 images using several GAN optimizations. We compare these synthetic images to the original dataset using similitude metrcis and we perform an psychovisual evaluation of the 240 images generated. Eight biological experts labeled the full dataset (280 images) as syntetic or natural using a custom-built software. We calculate the error rate per loss optimization as well as the hesitation time. We then compare these results to those provided by the similarity metrics. We test the psychovalidated images in a training step of a segmentation task.

Results and discussion

Generated images are considered as natural as the original dataset, with no increase of the hesitation time by experts. Experts are particularly misled by perceptual and Wasserstein loss optimization. These optimizations render the most qualitative and similar images according to metrics to the original dataset. We do not observe a strong correlation but links between some metrics and psychovisual decision according to the kind of generation. Particular Blur metric combinations could maybe replace the psychovisual evaluation. Segmentation task which use the most psychovalidated images are the most accurate.

A general deep learning framework for neuron instance segmentation based on Efficient UNet and morphological post-processing

Recent studies have demonstrated the superiority of deep learning in medical image analysis, especially in cell instance segmentation, a fundamental step for many biological studies. However, the excellent performance of the neural networks requires training on large, unbiased dataset and annotations, which is labor-intensive and expertise-demanding. This paper presents an end-to-end framework to automatically detect and segment NeuN stained neuronal cells on histological images using only point annotations. Unlike traditional nuclei segmentation with point annotation, we propose using point annotation and binary segmentation to synthesize pixel-level annotations. The synthetic masks are used as the ground truth to train the neural network, a U-Net-like architecture with a state-of-the-art network, EfficientNet, as the encoder. Validation results show the superiority of our model compared to other recent methods. In addition, we investigated multiple post-processing schemes and proposed an original strategy to convert the probability map into segmented instances using ultimate erosion and dynamic reconstruction. This approach is easy to configure and outperforms other classical post-processing techniques. This work aims to develop a robust and efficient framework for analyzing neurons using optical microscopic data, which can be used in preclinical biological studies and, more specifically, in the context of neurodegenerative diseases. Code is available at: https://github.com/MIRCen/NeuronInstanceSeg.

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.

Recent Trends and Perspectives in Cerebral Organoids Imaging and Analysis

Purpose: Since their first generation in 2013, the use of cerebral organoids has spread exponentially. Today, the amount of generated data is becoming challenging to analyze manually. This review aims to overview the current image acquisition methods and to subsequently identify the needs in image analysis tools for cerebral organoids. Methods: To address this question, we went through all recent articles published on the subject and annotated the protocols, acquisition methods, and algorithms used. Results: Over the investigated period of time, confocal microscopy and bright-field microscopy were the most used acquisition techniques. Cell counting, the most common task, is performed in 20% of the articles and area; around 12% of articles calculate morphological parameters. Image analysis on cerebral organoids is performed in majority using ImageJ software (around 52%) and Matlab language (4%). Treatments remain mostly semi-automatic. We highlight the limitations encountered in image analysis in the cerebral organoid field and suggest possible solutions and implementations to develop. Conclusions: In addition to providing an overview of cerebral organoids cultures and imaging, this work highlights the need to improve the existing image analysis methods for such images and the need for specific analysis tools. These solutions could specifically help to monitor the growth of future standardized cerebral organoids.

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.

Cortical thickness correlates of socioemotional difficulties in adults with Turner syndrome

Turner syndrome (TS) is a non-inherited genetic disorder associated with a specific cognitive phenotype and socioemotional impairments. The present study aimed at characterizing the neuroanatomical basis of socioemotional dysfunctions in TS using the Emotional Quotient inventory (EQ-I) and cortical morphology analysis in 17 individuals with TS (45,X) and 17-age and verbal IQ matched healthy females. Individuals with TS reported significantly greater socioemotional impairment than controls. Cortical thickness analysis showed that participants with TS had an overall thicker cortex than controls, with extensive alterations in the temporal, frontal, parietal and insular regions bilaterally. Using the total EQ-I score as regressor in the cortical thickness analysis revealed a number of brain regions where the relationship between cortical thickness and EQ-I score differed between groups; these areas included brain regions critically involved in socioemotional processes, such as bilateral insula, the anterior cingulate and the orbitofrontal cortex. These results show that socioemotional dysfunctions seen in women with TS are associated with significant alterations in brain morphology.

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.

Novel applications of quantitative MRI for the fetal brain

The advent of ultrafast MRI acquisitions is offering vital insights into the critical maturational events that occur throughout pregnancy. Concurrent with the ongoing enhancement of ultrafast imaging has been the development of innovative image-processing techniques that are enabling us to capture and quantify the exuberant growth, and organizational and remodeling processes that occur during fetal brain development. This paper provides an overview of the role of advanced neuroimaging techniques to study in vivo brain maturation and explores the application of a range of new quantitative imaging biomarkers that can be used clinically to monitor high-risk pregnancies.

Abnormal motor cortex excitability is associated with reduced cortical thickness in X monosomy

Turner syndrome (TS) is a noninherited genetic disorder caused by the absence of one or part of one X chromosome. It is characterized by physical and cognitive phenotypes that include motor deficits that may be related to neuroanatomical abnormalities of sensorimotor pathways. Here, we used transcranial magnetic stimulation (TMS) and cortical thickness analysis to assess motor cortex excitability and cortical morphology in 17 individuals with TS (45, X) and 17 healthy controls. Exploratory analysis was performed to detect the effect of parental origin of the X chromosome (X(mat), X(pat)) on both measures. Results showed that long-interval intracortical inhibition was reduced and motor threshold (MT) was increased in TS relative to controls. Areas of reduced thickness were observed in the precentral gyrus of individuals with TS that correlated with MT. A significant difference between X(mat) (n = 11) and X(pat) (n = 6) individuals was found on the measure of long-interval intracortical inhibition. These findings demonstrate the presence of converging anatomical and neurophysiological abnormalities of the motor system in X monosomy.

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.

A new cortical surface parcellation model and its automatic implementation

In this paper, we present an original method that aims at parcellating the cortical surface in regions functionally meaningful, from individual anatomy. The parcellation is obtained using an anatomically constrained surface-based coordinate system from which we define a complete partition of the surface. The aim of our method is to exhibit a new way to describe the cortical surface organization, in both anatomical and functional terms. The method is described together with results applied to a functional somatotopy experiments.

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.