Automatic Quantitative MRI Texture Analysis in Small-for-Gestational-Age Fetuses Discriminates Abnormal Neonatal Neurobehavior

Background

We tested the hypothesis whether texture analysis (TA) from MR images could identify patterns associated with an abnormal neurobehavior in small for gestational age (SGA) neonates.

Methods

Ultrasound and MRI were performed on 91 SGA fetuses at 37 weeks of GA. Frontal lobe, basal ganglia, mesencephalon and cerebellum were delineated from fetal MRIs. SGA neonates underwent NBAS test and were classified as abnormal if ≥1 area was <5^th centile and as normal if all areas were >5^th centile. Textural features associated with neurodevelopment were selected and machine learning was used to model a predictive algorithm.

Results

Of the 91 SGA neonates, 49 were classified as normal and 42 as abnormal. The accuracies to predict an abnormal neurobehavior based on TA were 95.12% for frontal lobe, 95.56% for basal ganglia, 93.18% for mesencephalon and 83.33% for cerebellum.

Conclusions

Fetal brain MRI textural patterns were associated with neonatal neurodevelopment. Brain MRI TA could be a useful tool to predict abnormal neurodevelopment in SGA.     

Magnetic Resonance Imaging of the Newborn Brain: Automatic Segmentation of Brain Images into 50 Anatomical Regions

We studied methods for the automatic segmentation of neonatal and developing brain images into 50 anatomical regions, utilizing a new set of manually segmented magnetic resonance (MR) images from 5 term-born and 15 preterm infants imaged at term corrected age called ALBERTs. Two methods were compared: individual registrations with label propagation and fusion; and template based registration with propagation of a maximum probability neonatal ALBERT (MPNA). In both cases we evaluated the performance of different neonatal atlases and MPNA, and the approaches were compared with the manual segmentations by means of the Dice overlap coefficient. Dice values, averaged across regions, were 0.81±0.02 using label propagation and fusion for the preterm population, and 0.81±0.02 using the single registration of a MPNA for the term population. Segmentations of 36 further unsegmented target images of developing brains yielded visibly high-quality results. This registration approach allows the rapid construction of automatically labeled age-specific brain atlases for neonates and the developing brain.     

Primary Microglia Isolation from Mixed Glial Cell Cultures of Neonatal Rat Brain Tissue

Microglia account for approximately 12% of the total cellular population in the mammalian brain. While neurons and astrocytes are considered the major cell types of the nervous system, microglia play a significant role in normal brain physiology by monitoring tissue for debris and pathogens and maintaining homeostasis in the parenchyma via phagocytic activity ^1,2. Microglia are activated during a number of injury and disease conditions, including neurodegenerative disease, traumatic brain injury, and nervous system infection ³. Under these activating conditions, microglia increase their phagocytic activity, undergo morpohological and proliferative change, and actively secrete reactive oxygen and nitrogen species, pro-inflammatory chemokines and cytokines, often activating a paracrine or autocrine loop ^4-6. As these microglial responses contribute to disease pathogenesis in neurological conditions, research focused on microglia is warranted.Due to the cellular heterogeneity of the brain, it is technically difficult to obtain sufficient microglial sample material with high purity during in vivo experiments. Current research on the neuroprotective and neurotoxic functions of microglia require a routine technical method to consistently generate pure and healthy microglia with sufficient yield for study. We present, in text and video, a protocol to isolate pure primary microglia from mixed glia cultures for a variety of downstream applications. Briefly, this technique utilizes dissociated brain tissue from neonatal rat pups to produce mixed glial cell cultures. After the mixed glial cultures reach confluency, primary microglia are mechanically isolated from the culture by a brief duration of shaking. The microglia are then plated at high purity for experimental study.The principle and protocol of this methodology have been described in the literature ^7,8. Additionally, alternate methodologies to isolate primary microglia are well described ^9-12. Homogenized brain tissue may be separated by density gradient centrifugation to yield primary microglia ¹². However, the centrifugation is of moderate length (45 min) and may cause cellular damage and activation, as well as, cause enriched microglia and other cellular populations. Another protocol has been utilized to isolate primary microglia in a variety of organisms by prolonged (16 hr) shaking while in culture ^9-11. After shaking, the media supernatant is centrifuged to isolate microglia. This longer two-step isolation method may also perturb microglial function and activation. We chiefly utilize the following microglia isolation protocol in our laboratory for a number of reasons: (1) primary microglia simulate in vivo biology more faithfully than immortalized rodent microglia cell lines, (2) nominal mechanical disruption minimizes potential cellular dysfunction or activation, and (3) sufficient yield can be obtained without passage of the mixed glial cell cultures.It is important to note that this protocol uses brain tissue from neonatal rat pups to isolate microglia and that using older rats to isolate microglia can significantly impact the yield, activation status, and functional properties of isolated microglia. There is evidence that aging is linked with microglia dysfunction, increased neuroinflammation and neurodegenerative pathologies, so previous studies have used ex vivo adult microglia to better understand the role of microglia in neurodegenerative diseases where aging is important parameter. However, ex vivo microglia cannot be kept in culture for prolonged periods of time. Therefore, while this protocol extends the life of primary microglia in culture, it should be noted that the microglia behave differently from adult microglia and in vitro studies should be carefully considered when translated to an in vivo setting.     

Automatic Structural Parcellation of Mouse Brain MRI Using Multi-Atlas Label Fusion

Multi-atlas segmentation propagation has evolved quickly in recent years, becoming a state-of-the-art methodology for automatic parcellation of structural images. However, few studies have applied these methods to preclinical research. In this study, we present a fully automatic framework for mouse brain MRI structural parcellation using multi-atlas segmentation propagation. The framework adopts the similarity and truth estimation for propagated segmentations (STEPS) algorithm, which utilises a locally normalised cross correlation similarity metric for atlas selection and an extended simultaneous truth and performance level estimation (STAPLE) framework for multi-label fusion. The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy. We showed that our multi-atlas framework resulted in significantly higher segmentation accuracy compared to single-atlas based segmentation, as well as to the original STAPLE framework.     

Automatic ROI Selection in Structural Brain MRI Using SOM 3D Projection

This paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map (SOM), generating a set of representative prototypes, and the receptive field (RF) of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer''s disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM). The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer''s Disease (AD). Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls (CN) and Alzheimer''s disease (AD) patients, and 84% of accuracy for Mild Cognitive Impairment (MCI) and AD patients.     

Brain Development after Neonatal Intermittent Hyperoxia-Hypoxia in the Rat Studied by Longitudinal MRI and Immunohistochemistry

Background

Neonatal intermittent hyperoxia-hypoxia (IHH) is involved in the pathogenesis of retinopathy of prematurity. Whether similar oxygen fluctuations will create pathological changes in the grey and white matter of the brain is unknown.

Methods

From birth until postnatal day 14 (P14), two litters (total n = 22) were reared in IHH: hyperoxia (50% O₂) interrupted by three consecutive two-minute episodes of hypoxia (12% O₂) every sixth hour. Controls (n = 8) were reared in room-air (20.9% O₂). Longitudinal MRI (Diffusion Tensor Imaging and T₂-mapping) was performed on P14 and P28 and retinal and brain tissue were examined for histopathological changes. Long-term neurodevelopment was assessed on P20 and P27.

Results

Mean, radial and axial diffusivity were higher in white matter of IHH versus controls at P14 (p < 0.04), while fractional anisotropy (FA) was lower in the hippocampal fimbria and tended to be lower in corpus callosum (p = 0.08) and external capsule (p = 0.05). White matter diffusivity in IHH was similar to controls at P28. Higher cortical vessel density (p = 0.005) was observed at P14. Cortical and thalamic T₂-relaxation time and mean diffusivity were higher in the IHH group at P14 (p ≤ 0.03), and albumin leakage was present at P28. Rats in the IHH group ran for a longer time on a Rotarod than the control group (p ≤ 0.005). Pups with lower bodyweight had more severe MRI alterations and albumin leakage.

Conclusion

IHH led to subtle reversible changes in brain white matter diffusivity, grey matter water content and vascular density. However, alterations in blood-brain barrier permeability may point to long-term effects. The changes seen after IHH exposure were more severe in animals with lower bodyweight and future studies should aim at exploring possible interactions between IHH and growth restriction.     

Electroporation-Based Treatment Planning for Deep-Seated Tumors Based on Automatic Liver Segmentation of MRI Images

Electroporation is the phenomenon that occurs when a cell is exposed to a high electric field, which causes transient cell membrane permeabilization. A paramount electroporation-based application is electrochemotherapy, which is performed by delivering high-voltage electric pulses that enable the chemotherapeutic drug to more effectively destroy the tumor cells. Electrochemotherapy can be used for treating deep-seated metastases (e.g. in the liver, bone, brain, soft tissue) using variable-geometry long-needle electrodes. To treat deep-seated tumors, patient-specific treatment planning of the electroporation-based treatment is required. Treatment planning is based on generating a 3D model of the organ and target tissue subject to electroporation (i.e. tumor nodules). The generation of the 3D model is done by segmentation algorithms. We implemented and evaluated three automatic liver segmentation algorithms: region growing, adaptive threshold, and active contours (snakes). The algorithms were optimized using a seven-case dataset manually segmented by the radiologist as a training set, and finally validated using an additional four-case dataset that was previously not included in the optimization dataset. The presented results demonstrate that patient''s medical images that were not included in the training set can be successfully segmented using our three algorithms. Besides electroporation-based treatments, these algorithms can be used in applications where automatic liver segmentation is required.     

A 3D Network Based Shape Prior for Automatic Myocardial Disease Segmentation in Delayed-Enhancement MRI

Objectives: In this work, a new deep learning model for relevant myocardial infarction segmentation from Late Gadolinium Enhancement (LGE)-MRI is proposed. Moreover, our novel segmentation method aims to detect microvascular-obstructed regions accurately. Material and methods: We first segment the anatomical structures, i.e., the left ventricular cavity and the myocardium, to achieve a preliminary segmentation. Then, a shape prior based framework that fuses the 3D U-Net architecture with 3D Autoencoder segmentation framework to constrain the segmentation process of pathological tissues is applied. Results: The proposed network reached outstanding myocardial segmentation compared with the human-level performance with the average Dice score of ‘0.9507’ for myocardium, ‘0.7656’ for scar, and ‘0.8377’ for MVO on the validation set consisting of 16 DE-MRI volumes selected from the training EMIDEC dataset. Conclusion: It is concluded that our approach's extensive validation and comprehensive comparison against existing state-of-the-art deep learning models on three annotated datasets, including healthy and diseased exams, make this proposal a reliable tool to enhance MI diagnosis.     

A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images

ObjectiveIn this research paper, the brain MRI images are going to classify by considering the excellence of CNN on a public dataset to classify Benign and Malignant tumors.Materials and MethodsDeep learning (DL) methods due to good performance in the last few years have become more popular for Image classification. Convolution Neural Network (CNN), with several methods, can extract features without using handcrafted models, and eventually, show better accuracy of classification. The proposed hybrid model combined CNN and support vector machine (SVM) in terms of classification and with threshold-based segmentation in terms of detection.ResultThe findings of previous studies are based on different models with their accuracy as Rough Extreme Learning Machine (RELM)-94.233%, Deep CNN (DCNN)-95%, Deep Neural Network (DNN) and Discrete Wavelet Autoencoder (DWA)-96%, k-nearest neighbors (kNN)-96.6%, CNN-97.5%. The overall accuracy of the hybrid CNN-SVM is obtained as 98.4959%.ConclusionIn today's world, brain cancer is one of the most dangerous diseases with the highest death rate, detection and classification of brain tumors due to abnormal growth of cells, shapes, orientation, and the location is a challengeable task in medical imaging. Magnetic resonance imaging (MRI) is a typical method of medical imaging for brain tumor analysis. Conventional machine learning (ML) techniques categorize brain cancer based on some handicraft property with the radiologist specialist choice. That can lead to failure in the execution and also decrease the effectiveness of an Algorithm. With a brief look came to know that the proposed hybrid model provides more effective and improvement techniques for classification.     

Tissue Distribution of Anidulafungin in Neonatal Rats

Anidulafungin, an echinocandin, is currently approved for treatment of fungal infections in adults. There is a high unmet medical need for treatment of fungal infections in neonatal patients, who may be at higher risk of infections involving bone, brain, and heart tissues. This in vivo preclinical study investigated anidulafungin distribution in plasma, bone, brain, and heart tissues in neonatal rats. Postnatal day (PND) 4 and PND 8 Fischer (F344/DuCrl) rats were dosed subcutaneously once with anidulafungin (10 mg/kg) or once daily for 5 days (PND 4–8). Plasma and tissue samples were collected and anidulafungin levels were measured by liquid chromatography‐tandem mass spectrometry. The mean plasma C_max and AUC_0‐24 values were consistent with single‐dose plasma pharmacokinetics (dose normalized) reported previously for adult rats. Observed bone concentrations were similar to plasma concentrations regardless of dosing duration, with bone‐to‐plasma concentration ratios of approximately 1.0. Heart concentrations were higher than plasma, with heart to plasma concentration ratios of 1.3‐ to 1.8‐fold. Brain concentrations were low after single dose, with brain‐to‐plasma concentration ratio of approximately 0.23, but increased to approximately 0.71 after 5 days of dosing. Tissue concentrations were nearly identical after single‐dose administration in both PND 4 and PND 8 animals, indicating that anidulafungin does not appear to differentially distribute in this period in neonatal rats. In conclusion, anidulafungin distributes to bone, brain, and heart tissues of neonatal rats; such results are supportive of further investigation of efficacy against infections involving bone, brain, and heart tissues.     

A Multi-Atlas Based Method for Automated Anatomical Rat Brain MRI Segmentation and Extraction of PET Activity

Introduction

Preclinical in vivo imaging requires precise and reproducible delineation of brain structures. Manual segmentation is time consuming and operator dependent. Automated segmentation as usually performed via single atlas registration fails to account for anatomo-physiological variability. We present, evaluate, and make available a multi-atlas approach for automatically segmenting rat brain MRI and extracting PET activies.

Methods

High-resolution 7T 2DT2 MR images of 12 Sprague-Dawley rat brains were manually segmented into 27-VOI label volumes using detailed protocols. Automated methods were developed with 7/12 atlas datasets, i.e. the MRIs and their associated label volumes. MRIs were registered to a common space, where an MRI template and a maximum probability atlas were created. Three automated methods were tested: 1/registering individual MRIs to the template, and using a single atlas (SA), 2/using the maximum probability atlas (MP), and 3/registering the MRIs from the multi-atlas dataset to an individual MRI, propagating the label volumes and fusing them in individual MRI space (propagation & fusion, PF). Evaluation was performed on the five remaining rats which additionally underwent [¹⁸F]FDG PET. Automated and manual segmentations were compared for morphometric performance (assessed by comparing volume bias and Dice overlap index) and functional performance (evaluated by comparing extracted PET measures).

Results

Only the SA method showed volume bias. Dice indices were significantly different between methods (PF>MP>SA). PET regional measures were more accurate with multi-atlas methods than with SA method.

Conclusions

Multi-atlas methods outperform SA for automated anatomical brain segmentation and PET measure’s extraction. They perform comparably to manual segmentation for FDG-PET quantification. Multi-atlas methods are suitable for rapid reproducible VOI analyses.     

Bleomycin-Detectable Iron in Brain Tissue

The normal brain contains regions with high concentrations of iron, part of which appears to be in a low molecular mass chelatable form. Iron complexes with a molecular mass of below 10,000, were measured in ultrafiltrates of homogenized gerbil brains using the bleomycin assay, and were found to average 20.5 ± 3.5 μM (n = 8). As expected, no bleomycin detectable iron was found in the plasma of these animals.

No obvious difference in the tissue levels of bleomycin-detectable iron was recorded following ischaemia and reperfusion. This is probably due to the already abundant presence of iron in the brain and the likely release of iron from protected sites due to structural damage inherent in the preparative procedures used.     

Automatic Segmentation of the Left Ventricle in Cardiac MRI Using Local Binary Fitting Model and Dynamic Programming Techniques

Segmentation of the left ventricle is very important to quantitatively analyze global and regional cardiac function from magnetic resonance. The aim of this study is to develop a novel algorithm for segmenting left ventricle on short-axis cardiac magnetic resonance images (MRI) to improve the performance of computer-aided diagnosis (CAD) systems. In this research, an automatic segmentation method for left ventricle is proposed on the basis of local binary fitting (LBF) model and dynamic programming techniques. The validation experiments are performed on a pool of data sets of 45 cases. For both endo- and epi-cardial contours of our results, percentage of good contours is about 93.5%, the average perpendicular distance are about 2 mm. The overlapping dice metric is about 0.91. The regression and determination coefficient between the experts and our proposed method on the LV mass is 1.038 and 0.9033, respectively; they are 1.076 and 0.9386 for ejection fraction (EF). The proposed segmentation method shows the better performance and has great potential in improving the accuracy of computer-aided diagnosis systems in cardiovascular diseases.     

Hypoxia Ischemia-Mediated Cell Death in Neonatal Rat Brain

The examination of Bcl-2-associated X protein (Bax) protein’s role in the activation of cognate nuclear, mitochondrial and ER cell death signaling cascades and the resulting effects on cell death phenotype in the brain after neonatal hypoxia-ischemia (HI) requires an understanding of neonatal HI insult and progression, as well as, its dysfunctional outcomes. In addition, knowledge of key concepts of oxidative stress, a major injurious component of HI, and the different cell death phenotypes (i.e. apoptosis and necrosis) will aid the design of appropriate useful experimental paradigms. Here we discuss organelle cell death signaling cascades in the context of the different cell death phenotypes associated with animal models of neonatal hypoxia ischemia and tissue culture models used in the study of hypoxia ischemia, focusing on the intracellular shifts of the Bcl-2 associated X protein (Bax) in the hypoxic brain. Special issue article in honor of Dr. Anna Maria Giuffrida-Stella.     

Systemic Evaluation of Hypoxic-Ischemic Brain Injury in Neonatal Rats

The objective of the study was to evaluate the systematically rat model of neonatal hypoxic-ischemic brain damage. The right carotid arteries of 7-day-old healthy Wistar rats were ligated, and then, the rats were subjected to an environment with 8 % of oxygen. Four weeks after the birth, neurobehavioral test, water maze test, and motor-evoked potential and neuropathologic examinations were performed. The footprint analysis showed significantly larger and instable paces in the hypoxic-ischemic group (P < 0.05); the time that rats crossed the balance beam in the hypoxic-ischemic group was longer than the control group (P < 0.05). The water maze test showed that the escape latency of hypoxic-ischemic group was significantly longer than that of control group (P < 0.05). The hindlimb quadriceps compound muscle-evoked potential CMEP of rats in hypoxic-ischemic group showed that the wave amplitude was lower than that of control group (P < 0.05). HE staining showed visible periventricular leukomalacia in hypoxic-ischemic groups; disrupted nuclear membrane was detected in the IH group with transelectronmicroscopy; Immunohistochemistry: compared with control group, MBP-positive neurocytes decreased, glial fibrillary acidic protein positive neurocytes increased in the periventricular zone (P < 0.05). Carotid artery ligation combining the hypoxic chamber created a reliable and stable rat model of neonatal hypoxic-ischemic brain damage and can be used for experimental research related to management of cerebral palsy.     

ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM

Particle picking is currently a critical step in the cryo-electron microscopy single particle reconstruction pipeline. Contaminations in the acquired micrographs severely degrade the performance of particle pickers, resulting in many “non-particles” in the collected stack of particles. In this paper, we present ASOCEM (Automatic Segmentation Of Contaminations in cryo-EM), an automatic method to detect and segment contaminations, which requires as an input only the approximate particle size. In particular, it does not require any parameter tuning nor manual intervention. Our method is based on the observation that the statistical distribution of contaminated regions is different from that of the rest of the micrograph. This nonrestrictive assumption allows to automatically detect various types of contaminations, from the carbon edges of the supporting grid to high contrast blobs of different sizes. We demonstrate the efficiency of our algorithm using various experimental data sets containing various types of contaminations. ASOCEM is integrated as part of the KLT picker (Eldar et al., 2020) and is available at https://github.com/ShkolniskyLab/kltpicker2.     

Two-color Fluorescence Labeling in Acrolein-fixed Brain Tissue

Acrolein is a potent fixative that provides both excellent preservation of ultrastructural morphology and retention of antigenicity, thus it is frequently used for immunocytochemical detection of antigens at the electron microscopic level. However, acrolein is not commonly used for fluorescence microscopy because of concerns about possible autofluorescence and destruction of the luminosity of fluorescent dyes. Here we describe a simple protocol that allows fine visualization of two fluorescent markers in 40-μm sections from acrolein-perfused rat brain. Autofluorescence was removed by pretreatment with 1% sodium borohydride for 30 min, and subsequent incubation in a 50% ethanol solution containing 0.3% hydrogen peroxide enhanced fluorescence labeling. Thus, fluorescence labeling can be used for high-quality detection of markers in tissue perfused with acrolein. Furthermore, adjacent acrolein-fixed sections from a single experiment can be processed to produce high-quality results for electron microscopy or fluorescence labeling. (J Histochem Cytochem 58:359–368, 2010)     

脑多发性硬化的MRI诊断

目的:探讨脑内多发性硬化(MS)的核磁共振(MRI)诊断价值.方法:回顾性分析48例经临床确诊为MS患者的MRI表现及相关资料.48例均行MRI常规平扫,46例加做增强扫描.结果:(1)病灶多发,主要分布于侧脑室周围及额、颞、顶、枕叶皮层下白质区,部分累及胼胝体、小脑、脑干、丘脑、基底节及大脑皮质;双侧侧脑室旁可见“垂直脱髓鞘征”;(2)病灶主要以长和较长T2、略长和等T1信号为主;(3)增强扫描显示强化与不强化病灶同时存在,强化病灶呈点状、斑片及环形;少数急性起病病例病灶呈较均匀一致的强化.结论:MRI是临床诊断MS的敏感、直观、最有价值的检查手段.     

铅损伤对新生鼠脑组织中多胺的影响

将新生Wistar大鼠按腹腔注射醋酸铅的剂量 ,随机分为正常、低铅组、高铅组。各组注射剂量分别为 0 ,2 0 ,6 0mg/kg。定期测量体重 ,采用薄板层析法测脑中精脒的含量。结果显示正常组出生后体重增长迅速 ;与正常组相比 ,高、低铅组分别于出生 5、10天后体重增长明显落后 ;脑重、脑精脒含量均显著低于正常组 ,并且与铅损伤的剂量有明显的负相关 (r =- 0 .793,P <0 .0 1)。研究表明脑中多胺的量是反映铅神经毒性程度的有用指标