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Detection of Schizophrenia from EEG Signals using Selected Statistical Moments of MFC Coefficients and Ensemble Learning 利用 MFC 系数的选定统计矩和集合学习从脑电图信号中检测精神分裂症
IF 3 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-19 DOI: 10.1007/s12021-024-09684-4
Ashima Tyagi, Vibhav Prakash Singh, Manoj Madhava Gore

Schizophrenia is a mental disorder characterized by neurophysiological dysfunctions that result in disturbances in thinking, perception, and behavior. Early identification of schizophrenia can help prevent potential complications and facilitate effective treatment and management of the condition. This paper proposes a computer aided diagnosis system for the early detection of schizophrenia using 19-channel Electroencephalography (EEG) signals from 28 subjects, leveraging statistical moments of Mel-frequency Cepstral Coefficients (MFCC) and ensemble learning. Initially, the EEG signals are passed through a high-pass filter to mitigate noise and remove extraneous data. The feature extraction technique is then employed to extract MFC coefficients from the filtered EEG signals. The dimensionality of these coefficients is reduced by computing their statistical moments, which include the mean, standard deviation, skewness, kurtosis, and energy. Subsequently, the Support Vector Machine based Recursive Feature Elimination (SVM-RFE) is applied to identify pertinent features from the statistical moments of the MFC coefficients. These SVM-RFE-based selected features serve as input for three base classifiers: Support Vector Machine, k-Nearest Neighbors, and Logistic Regression. Additionally, an ensemble learning approach, which combines the predictions of the three classifiers through majority voting, is introduced to enhance schizophrenia detection performance and generalize the results of the proposed approach. The study’s findings demonstrate that the ensemble model, combined with SVM-RFE-based selected statistical moments of MFCC, achieves encouraging detection performance, highlighting the potential of machine learning techniques in advancing the diagnostic process of schizophrenia.

精神分裂症是一种精神障碍,其特征是神经生理功能失调,导致思维、感知和行为紊乱。早期发现精神分裂症有助于预防潜在的并发症,并促进有效的治疗和管理。本文提出了一种计算机辅助诊断系统,利用 Mel-frequency Cepstral Coefficients (MFCC) 的统计矩和集合学习,通过 28 名受试者的 19 个通道的脑电图(EEG)信号,对精神分裂症进行早期检测。首先,脑电信号经过高通滤波器,以减少噪音和去除无关数据。然后采用特征提取技术从滤波后的脑电信号中提取 MFC 系数。通过计算这些系数的统计矩(包括平均值、标准偏差、偏斜度、峰度和能量)来降低其维度。随后,应用基于支持向量机的递归特征消除(SVM-RFE)从 MFC 系数的统计矩中识别相关特征。这些基于 SVM-RFE 的选定特征可作为三个基础分类器的输入:支持向量机、k-近邻和逻辑回归。此外,还引入了一种集合学习方法,通过多数投票将三个分类器的预测结果结合起来,以提高精神分裂症的检测性能,并推广所提议方法的结果。研究结果表明,集合模型结合基于 SVM-RFE 的 MFCC 选定统计矩,取得了令人鼓舞的检测性能,凸显了机器学习技术在推进精神分裂症诊断过程中的潜力。
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引用次数: 0
MBV-Pipe: A One-Stop Toolbox for Assessing Mouse Brain Morphological Changes for Cross-Scale Studies MBV-Pipe:用于跨尺度研究的小鼠脑形态变化评估一站式工具箱
IF 3 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-16 DOI: 10.1007/s12021-024-09687-1
Wentao Jiang, Xinyi Liu, Ming Song, Zhengyi Yang, Lan Sun, Tianzi Jiang

Mouse models are crucial for neuroscience research, yet discrepancies arise between macro- and meso-scales due to sample preparation altering brain morphology. The absence of an accessible toolbox for magnetic resonance imaging (MRI) data processing presents a challenge for assessing morphological changes in the mouse brain. To address this, we developed the MBV-Pipe (Mouse Brain Volumetric Statistics-Pipeline) toolbox, integrating the methods of Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL)-Voxel-based morphometry (VBM) and Tract-Based Spatial Statistics (TBSS) to evaluate brain tissue volume and white matter integrity. To validate the reliability of MBV-Pipe, brain MRI data from seven mice at three time points (in vivo, post-perfusion, and post-fixation) were acquired using a 9.4T ultra-high MRI system. Employing the MBV-Pipe toolbox, we discerned substantial volumetric changes in the mouse brain following perfusion relative to the in vivo condition, with the fixation process inducing only negligible variations. Importantly, the white matter integrity was found to be largely stable throughout the sample preparation procedures. The MBV-Pipe source code is publicly available and includes a user-friendly GUI for facilitating quality control and experimental protocol optimization, which holds promise for advancing mouse brain research in the future.

小鼠模型对神经科学研究至关重要,但由于样本制备会改变大脑形态,因此宏观和中观尺度之间存在差异。磁共振成像(MRI)数据处理工具箱的缺乏给评估小鼠大脑形态变化带来了挑战。为了解决这个问题,我们开发了 MBV-Pipe(小鼠脑容量统计管道)工具箱,它整合了通过幂级数列代数(DARTEL)进行的差形解剖学注册-基于体素的形态测量(VBM)和基于瓣膜的空间统计(TBSS)方法,用于评估脑组织体积和白质完整性。为了验证 MBV-Pipe 的可靠性,我们使用 9.4T 超高磁共振成像系统采集了七只小鼠在三个时间点(体内、灌注后和固定后)的脑磁共振成像数据。利用 MBV-Pipe 工具箱,我们发现灌注后小鼠大脑的体积相对于体内状态发生了很大变化,而固定过程引起的变化可以忽略不计。重要的是,在整个样本制备过程中,白质的完整性基本保持稳定。MBV-Pipe 的源代码是公开的,包括一个用户友好的图形用户界面,便于质量控制和实验方案优化,有望在未来推动小鼠大脑研究。
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引用次数: 0
Morphology and Texture-Guided Deep Neural Network for Intracranial Aneurysm Segmentation in 3D TOF-MRA 用于三维 TOF-MRA 颅内动脉瘤分割的形态学和纹理引导的深度神经网络
IF 3 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-11 DOI: 10.1007/s12021-024-09683-5
Maysam Orouskhani, Negar Firoozeh, Huayu Wang, Yan Wang, Hanrui Shi, Weijing Li, Beibei Sun, Jianjian Zhang, Xiao Li, Huilin Zhao, Mahmud Mossa-Basha, Jenq-Neng Hwang, Chengcheng Zhu

This study concentrates on the segmentation of intracranial aneurysms, a pivotal aspect of diagnosis and treatment planning. We aim to overcome the inherent instance imbalance and morphological variability by introducing a novel morphology and texture loss reweighting approach. Our innovative method involves the incorporation of tailored weights within the loss function of deep neural networks. Specifically designed to account for aneurysm size, shape, and texture, this approach strategically guides the model to focus on capturing discriminative information from imbalanced features. The study conducted extensive experimentation utilizing ADAM and RENJI TOF-MRA datasets to validate the proposed approach. The results of our experimentation demonstrate the remarkable effectiveness of the introduced methodology in improving aneurysm segmentation accuracy. By dynamically adapting to the variances present in aneurysm features, our model showcases promising outcomes for accurate diagnostic insights. The nuanced consideration of morphological and textural nuances within the loss function proves instrumental in overcoming the challenge posed by instance imbalance. In conclusion, our study presents a nuanced solution to the intricate challenge of intracranial aneurysm segmentation. The proposed morphology and texture loss reweighting approach, with its tailored weights and dynamic adaptability, proves to be instrumental in enhancing segmentation precision. The promising outcomes from our experimentation suggest the potential for accurate diagnostic insights and informed treatment strategies, marking a significant advancement in this critical domain of medical imaging.

本研究集中于颅内动脉瘤的分割,这是诊断和治疗计划的一个关键方面。我们旨在通过引入一种新颖的形态和纹理损失再加权方法来克服固有的实例不平衡和形态可变性。我们的创新方法是在深度神经网络的损失函数中加入量身定制的权重。这种方法专门针对动脉瘤的大小、形状和纹理而设计,可战略性地引导模型重点捕捉不平衡特征中的判别信息。研究利用 ADAM 和 RENJI TOF-MRA 数据集进行了广泛的实验,以验证所提出的方法。实验结果表明,所引入的方法在提高动脉瘤分割准确性方面效果显著。通过动态适应动脉瘤特征中存在的差异,我们的模型为准确诊断提供了可喜的成果。事实证明,在损失函数中对形态和纹理细微差别的细致考虑有助于克服实例不平衡带来的挑战。总之,我们的研究针对颅内动脉瘤分割这一错综复杂的难题提出了一种细致入微的解决方案。所提出的形态和纹理损失再加权方法具有量身定制的权重和动态适应性,被证明有助于提高分割精度。我们的实验取得了令人鼓舞的成果,这表明我们有可能获得准确的诊断见解和明智的治疗策略,这标志着医学成像这一关键领域取得了重大进展。
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引用次数: 0
Understanding Learning from EEG Data: Combining Machine Learning and Feature Engineering Based on Hidden Markov Models and Mixed Models 从脑电图数据中理解学习:基于隐马尔可夫模型和混合模型的机器学习与特征工程相结合
IF 3 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-09-10 DOI: 10.1007/s12021-024-09690-6
Gabriel R. Palma, Conor Thornberry, Seán Commins, Rafael A. Moral

Theta oscillations, ranging from 4-8 Hz, play a significant role in spatial learning and memory functions during navigation tasks. Frontal theta oscillations are thought to play an important role in spatial navigation and memory. Electroencephalography (EEG) datasets are very complex, making any changes in the neural signal related to behaviour difficult to interpret. However, multiple analytical methods are available to examine complex data structures, especially machine learning-based techniques. These methods have shown high classification performance, and their combination with feature engineering enhances their capability. This paper proposes using hidden Markov and linear mixed effects models to extract features from EEG data. Based on the engineered features obtained from frontal theta EEG data during a spatial navigation task in two key trials (first, last) and between two conditions (learner and non-learner), we analysed the performance of six machine learning methods on classifying learner and non-learner participants. We also analysed how different standardisation methods used to pre-process the EEG data contribute to classification performance. We compared the classification performance of each trial with data gathered from the same subjects, including solely coordinate-based features, such as idle time and average speed. We found that more machine learning methods perform better classification using coordinate-based data. However, only deep neural networks achieved an area under the ROC curve higher than 80% using the theta EEG data alone. Our findings suggest that standardising the theta EEG data and using deep neural networks enhances the classification of learner and non-learner subjects in a spatial learning task.

4-8赫兹的θ振荡在导航任务中的空间学习和记忆功能中发挥着重要作用。额叶θ振荡被认为在空间导航和记忆中发挥着重要作用。脑电图(EEG)数据集非常复杂,因此很难解释与行为相关的神经信号变化。不过,目前有多种分析方法可用于研究复杂的数据结构,特别是基于机器学习的技术。这些方法显示出很高的分类性能,与特征工程的结合增强了它们的能力。本文建议使用隐马尔可夫模型和线性混合效应模型从脑电图数据中提取特征。基于在两次关键试验(第一次和最后一次)和两种条件(学习者和非学习者)下进行空间导航任务时从额叶θ脑电图数据中获得的工程特征,我们分析了六种机器学习方法在对学习者和非学习者参与者进行分类时的性能。我们还分析了用于预处理脑电图数据的不同标准化方法对分类性能的影响。我们将每次试验的分类性能与从相同受试者处收集的数据进行了比较,其中仅包括基于坐标的特征,如空闲时间和平均速度。我们发现,使用基于坐标的数据,更多机器学习方法的分类效果更好。然而,只有深度神经网络在仅使用θ EEG 数据时,其 ROC 曲线下面积高于 80%。我们的研究结果表明,将θ脑电图数据标准化并使用深度神经网络可增强空间学习任务中学习者和非学习者的分类。
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引用次数: 0
AnNoBrainer, An Automated Annotation of Mouse Brain Images using Deep Learning. AnNoBrainer,利用深度学习自动标注小鼠大脑图像。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-08-07 DOI: 10.1007/s12021-024-09679-1
Roman Peter, Petr Hrobar, Josef Navratil, Martin Vagenknecht, Jindrich Soukup, Keiko Tsuji, Nestor X Barrezueta, Anna C Stoll, Renee C Gentzel, Jonathan A Sugam, Jacob Marcus, Danny A Bitton

Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints in neuroscience digital pathology. Prior experience and domain expert knowledge are the key aspects for image annotation quality and consistency. At present, image annotation is often achieved manually by certified pathologists or trained technicians, limiting the total throughput of studies performed at neuroscience digital pathology labs. It may also mean that simpler and quicker methods of examining tissue samples are used by non-pathologists, especially in the early stages of research and preclinical studies. To address these limitations and to meet the growing demand for image analysis in a pharmaceutical setting, we developed AnNoBrainer, an open-source software tool that leverages deep learning, image registration, and standard cortical brain templates to automatically annotate individual brain regions on 2D pathology slides. Application of AnNoBrainer to a published set of pathology slides from transgenic mice models of synucleinopathy revealed comparable accuracy, increased reproducibility, and a significant reduction (~ 50%) in time spent on brain annotation, quality control and labelling compared to trained scientists in pathology. Taken together, AnNoBrainer offers a rapid, accurate, and reproducible automated annotation of mouse brain images that largely meets the experts' histopathological assessment standards (> 85% of cases) and enables high-throughput image analysis workflows in digital pathology labs.

对整个小鼠大脑的多个感兴趣区域进行标注是对神经科学数字病理学中的多种研究终点进行定量评估的一个不可或缺的过程。事先经验和领域专家知识是保证图像标注质量和一致性的关键因素。目前,图像注释通常由经过认证的病理学家或训练有素的技术人员手工完成,这限制了神经科学数字病理实验室的研究总吞吐量。这也可能意味着非病理学家会使用更简单快捷的方法来检查组织样本,尤其是在研究和临床前研究的早期阶段。为了解决这些局限性并满足制药领域对图像分析日益增长的需求,我们开发了 AnNoBrainer,这是一款开源软件工具,它利用深度学习、图像注册和标准皮层脑模板自动注释二维病理切片上的单个脑区。将 AnNoBrainer 应用于一组已发表的突触核蛋白病转基因小鼠模型病理切片后发现,与经过培训的病理学科学家相比,AnNoBrainer 的准确性相当高,可重复性也有所提高,而且在大脑注释、质量控制和标记方面所花费的时间显著减少(约 50%)。总之,AnNoBrainer 提供了一种快速、准确、可重复的小鼠大脑图像自动标注方法,在很大程度上达到了专家的组织病理学评估标准(> 85% 的病例),并实现了数字病理实验室的高通量图像分析工作流程。
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引用次数: 0
Assessment of Sports Concussion in Female Athletes: A Role for Neuroinformatics? 评估女运动员的运动性脑震荡:神经信息学的作用?
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-30 DOI: 10.1007/s12021-024-09680-8
Rachel Edelstein, Sterling Gutterman, Benjamin Newman, John Darrell Van Horn

Over the past decade, the intricacies of sports-related concussions among female athletes have become readily apparent. Traditional clinical methods for diagnosing concussions suffer limitations when applied to female athletes, often failing to capture subtle changes in brain structure and function. Advanced neuroinformatics techniques and machine learning models have become invaluable assets in this endeavor. While these technologies have been extensively employed in understanding concussion in male athletes, there remains a significant gap in our comprehension of their effectiveness for female athletes. With its remarkable data analysis capacity, machine learning offers a promising avenue to bridge this deficit. By harnessing the power of machine learning, researchers can link observed phenotypic neuroimaging data to sex-specific biological mechanisms, unraveling the mysteries of concussions in female athletes. Furthermore, embedding methods within machine learning enable examining brain architecture and its alterations beyond the conventional anatomical reference frame. In turn, allows researchers to gain deeper insights into the dynamics of concussions, treatment responses, and recovery processes. This paper endeavors to address the crucial issue of sex differences in multimodal neuroimaging experimental design and machine learning approaches within female athlete populations, ultimately ensuring that they receive the tailored care they require when facing the challenges of concussions. Through better data integration, feature identification, knowledge representation, validation, etc., neuroinformaticists, are ideally suited to bring clarity, context, and explainabilty to the study of sports-related head injuries in males and in females, and helping to define recovery.

在过去的十年中,女性运动员中与运动相关的脑震荡的复杂性已变得显而易见。传统的脑震荡临床诊断方法在应用于女运动员时存在局限性,往往无法捕捉到大脑结构和功能的细微变化。先进的神经信息学技术和机器学习模型已成为这方面的宝贵财富。虽然这些技术已被广泛应用于了解男性运动员的脑震荡情况,但我们对其对女性运动员的有效性的理解仍有很大差距。凭借出色的数据分析能力,机器学习为弥补这一不足提供了一条大有可为的途径。通过利用机器学习的强大功能,研究人员可以将观察到的表型神经影像数据与性别特异性生物机制联系起来,从而揭开女运动员脑震荡的神秘面纱。此外,在机器学习中嵌入方法,可以超越传统的解剖参考框架,检查大脑结构及其变化。反过来,研究人员也能更深入地了解脑震荡的动态变化、治疗反应和恢复过程。本文致力于解决多模态神经成像实验设计和机器学习方法在女性运动员群体中的性别差异这一关键问题,最终确保她们在面对脑震荡挑战时获得所需的定制护理。通过更好的数据整合、特征识别、知识表示、验证等,神经信息学家非常适合为男性和女性运动相关头部损伤的研究带来清晰度、背景和可解释性,并帮助确定康复。
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引用次数: 0
Characteristics of the Structural Connectivity in Patients with Brain Injury and Chronic Health Symptoms: A Pilot Study. 脑损伤和慢性健康症状患者的结构连通性特征:一项试点研究
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-11 DOI: 10.1007/s12021-024-09681-7
Xiaojian Kang, Byung C Yoon, Emily Grossner, Maheen M Adamson

Diffusion properties from diffusion tensor imaging (DTI) are exquisitely sensitive to white matter abnormalities incurred during traumatic brain injury (TBI), especially for those patients with chronic post-TBI symptoms such as headaches, dizziness, fatigue, etc. The evaluation of structural and functional connectivity using DTI has become a promising method for identifying subtle alterations in brain connectivity associated with TBI that are otherwise not visible with conventional imaging. This study assessed whether TBI patients with (n = 17) or without (n = 16) chronic symptoms (TBIcs/TBIncs) exhibit any changes in structural connectivity (SC) and mean fractional anisotropy (mFA) of intra- and inter-hemispheric connections when compared to a control group (CG) (n = 13). Reductions in SC and mFA were observed for TBIcs compared to CG, but not for TBIncs. More connections were found to have mFA reductions than SC reductions. On the whole, SC is dominated by ipsilateral connections for all the groups after the comparison of contralateral and ipsilateral connections. More contra-ipsi reductions of mFA were found for TBIcs than TBIncs compared to CG. These findings suggest that TBI patients with chronic symptoms not only demonstrate decreased global and regional mFA but also reduced structural network connectivity.

通过弥散张量成像(DTI)获得的弥散特性对创伤性脑损伤(TBI)期间出现的白质异常非常敏感,尤其是对那些有头痛、头晕、疲劳等 TBI 后慢性症状的患者。使用 DTI 评估结构和功能连通性已成为一种很有前途的方法,可用于识别与 TBI 相关的大脑连通性的细微改变,而这些改变在传统成像中是看不到的。本研究评估了与对照组(CG)(n = 13)相比,有(n = 17)或无(n = 16)慢性症状(TBIcs/TBIncs)的 TBI 患者在半球内和半球间连接的结构连通性(SC)和平均分数各向异性(mFA)方面是否有任何变化。与对照组相比,观察到 TBIcs 的 SC 和 mFA 下降,但 TBIncs 没有下降。与 SC 的减少相比,发现有更多连接的 mFA 减少。总体而言,在对比对侧和同侧连接后,所有组别的 SC 均以同侧连接为主。与 CG 相比,TBIcs 比 TBIncs 的 mFA 减少更多。这些研究结果表明,有慢性症状的创伤性脑损伤患者不仅表现出整体和区域性 mFA 的减少,而且还表现出结构性网络连接的减少。
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引用次数: 0
Classifying Neuronal Cell Types Based on Shared Electrophysiological Information from Humans and Mice. 根据人类和小鼠共享的电生理信息对神经元细胞类型进行分类
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-08 DOI: 10.1007/s12021-024-09675-5
Ofek Ophir, Orit Shefi, Ofir Lindenbaum

The brain is an intricate system that controls a variety of functions. It consists of a vast number of cells that exhibit diverse characteristics. To understand brain function in health and disease, it is crucial to classify neurons accurately. Recent advancements in machine learning have provided a way to classify neurons based on their electrophysiological activity. This paper presents a deep-learning framework that classifies neurons solely on this basis. The framework uses data from the Allen Cell Types database, which contains a survey of biological features derived from single-cell recordings from mice and humans. The shared information from both sources is used to classify neurons into their broad types with the help of a joint model. An accurate domain-adaptive model, integrating electrophysiological data from both mice and humans, is implemented. Furthermore, data from mouse neurons, which also includes labels of transgenic mouse lines, is further classified into subtypes using an interpretable neural network model. The framework provides state-of-the-art results in terms of accuracy and precision while also providing explanations for the predictions.

大脑是一个控制各种功能的复杂系统。它由大量细胞组成,这些细胞表现出不同的特征。要了解大脑在健康和疾病中的功能,对神经元进行准确分类至关重要。机器学习领域的最新进展为根据神经元的电生理活动对其进行分类提供了一种方法。本文介绍了一种深度学习框架,该框架可完全在此基础上对神经元进行分类。该框架使用来自艾伦细胞类型数据库的数据,该数据库包含从小鼠和人类单细胞记录中提取的生物特征调查。在联合模型的帮助下,来自这两个来源的共享信息被用于将神经元划分为广泛的类型。我们建立了一个精确的领域自适应模型,整合了小鼠和人类的电生理数据。此外,来自小鼠神经元的数据(也包括转基因小鼠品系的标签)也利用可解释的神经网络模型进一步划分为亚型。该框架在准确度和精确度方面提供了最先进的结果,同时还为预测提供了解释。
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引用次数: 0
Photogrammetry scans for neuroanatomy education - a new multi-camera system: technical note. 用于神经解剖学教育的摄影测量扫描--新型多摄像头系统:技术说明。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-01 Epub Date: 2024-06-13 DOI: 10.1007/s12021-024-09672-8
André S B Oliveira, Luciano C P C Leonel, Megan M J Bauman, Alessandro De Bonis, Edward R LaHood, Stephen Graepel, Michael J Link, Carlos D Pinheiro-Neto, Nirusha Lachman, Jonathan M Morris, Maria Peris-Celda

Photogrammetry scans has directed attention to the development of advanced camera systems to improve the creation of three-dimensional (3D) models, especially for educational and medical-related purposes. This could be a potential cost-effective method for neuroanatomy education, especially when access to laboratory-based learning is limited. The aim of this study was to describe a new photogrammetry system based on a 5 Digital Single-Lens Reflex (DSLR) cameras setup to optimize accuracy of neuroanatomical 3D models. One formalin-fixed brain and specimen and one dry skull were used for dissections and scanning using the photogrammetry technique. After each dissection, the specimens were placed inside a new MedCreator® scanner (MedReality, Thyng, Chicago, IL) to be scanned with the final 3D model being displayed on SketchFab® (Epic, Cary, NC) and MedReality® platforms. The scanner consisted of 5 cameras arranged vertically facing the specimen, which was positioned on a platform in the center of the scanner. The new multi-camera system contains automated software packages, which allowed for quick rendering and creation of a high-quality 3D models. Following uploading the 3D models to the SketchFab® and MedReality® platforms for display, the models can be freely manipulated in various angles and magnifications in any devices free of charge for users. Therefore, photogrammetry scans with this new multi-camera system have the potential to enhance the accuracy and resolution of the 3D models, along with shortening creation time of the models. This system can serve as an important tool to optimize neuroanatomy education and ultimately, improve patient outcomes.

摄影测量扫描将人们的注意力引向了先进摄像系统的开发,以改进三维(3D)模型的创建,尤其是用于教育和医疗相关目的。这可能是神经解剖学教育中一种潜在的具有成本效益的方法,尤其是在实验室学习机会有限的情况下。本研究旨在描述一种基于 5 台数码单反相机(DSLR)的新型摄影测量系统,以优化神经解剖三维模型的精确度。使用摄影测量技术对一个福尔马林固定的大脑和标本以及一个干燥的头骨进行解剖和扫描。每次解剖后,将标本放入新型 MedCreator® 扫描仪(MedReality, Thyng, Chicago, IL)中进行扫描,最终的三维模型将显示在 SketchFab® (Epic, Cary, NC) 和 MedReality® 平台上。该扫描仪由 5 台相机组成,相机垂直朝向标本,标本被放置在扫描仪中心的平台上。新的多摄像头系统包含自动软件包,可快速渲染和创建高质量的三维模型。将三维模型上传到 SketchFab® 和 MedReality® 平台显示后,用户可以在任何设备上以各种角度和放大倍率自由操作模型,而且不收取任何费用。因此,使用这种新型多相机系统进行摄影测量扫描有可能提高三维模型的精确度和分辨率,同时缩短模型的创建时间。该系统可作为优化神经解剖学教育的重要工具,并最终改善患者的治疗效果。
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引用次数: 0
Solving the Pervasive Problem of Protocol Non-Compliance in MRI using an Open-Source tool mrQA. 使用开源工具 mrQA 解决核磁共振成像中普遍存在的不遵守协议问题。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-07-01 Epub Date: 2024-06-11 DOI: 10.1007/s12021-024-09668-4
Harsh Sinha, Pradeep Reddy Raamana

Pooling data across diverse sources acquired by multisite consortia requires compliance with a predefined reference protocol i.e., ensuring different sites and scanners for a given project have used identical or compatible MR physics parameter values. Traditionally, this has been an arduous and manual process due to difficulties in working with the complicated DICOM standard and lack of resources allocated towards protocol compliance. Moreover, issues of protocol compliance is often overlooked for lack of realization that parameter values are routinely improvised/modified locally at various sites. The inconsistencies in acquisition protocols can reduce SNR, statistical power, and in the worst case, may invalidate the results altogether. An open-source tool, mrQA was developed to automatically assess protocol compliance on standard dataset formats such as DICOM and BIDS, and to study the patterns of non-compliance in over 20 open neuroimaging datasets, including the large ABCD study. The results demonstrate that the lack of compliance is rather pervasive. The frequent sources of non-compliance include but are not limited to deviations in Repetition Time, Echo Time, Flip Angle, and Phase Encoding Direction. It was also observed that GE and Philips scanners exhibited higher rates of non-compliance relative to the Siemens scanners in the ABCD dataset. Continuous monitoring for protocol compliance is strongly recommended before any pre/post-processing, ideally right after the acquisition, to avoid the silent propagation of severe/subtle issues. Although, this study focuses on neuroimaging datasets, the proposed tool mrQA can work with any DICOM-based datasets.

汇集多站点联盟获取的不同来源的数据需要符合预定义的参考协议,即确保特定项目的不同站点和扫描仪使用相同或兼容的磁共振物理参数值。传统上,由于难以使用复杂的 DICOM 标准和缺乏用于协议合规的资源,这是一个艰巨的手动过程。此外,由于没有意识到参数值在不同地点经常会被临时修改,协议合规性问题经常被忽视。采集协议的不一致会降低信噪比和统计功率,最糟糕的情况是,可能会导致结果完全失效。我们开发了一款开源工具 mrQA,用于自动评估 DICOM 和 BIDS 等标准数据集格式的协议合规性,并研究了包括大型 ABCD 研究在内的 20 多个开放式神经成像数据集的不合规模式。研究结果表明,不遵守协议的现象相当普遍。不合规的常见原因包括但不限于重复时间、回波时间、翻转角度和相位编码方向的偏差。在 ABCD 数据集中还观察到,相对于西门子扫描仪,通用电气和飞利浦扫描仪的违规率更高。强烈建议在进行任何前/后处理之前,最好是在采集后立即对协议合规性进行持续监控,以避免严重/细微问题的无声传播。虽然这项研究的重点是神经成像数据集,但建议使用的 mrQA 工具可以处理任何基于 DICOM 的数据集。
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Neuroinformatics
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