Neuroinformatics最新文献_第5页

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

Neuroinformatics 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

{"title":"Morphology and Texture-Guided Deep Neural Network for Intracranial Aneurysm Segmentation in 3D TOF-MRA","authors":"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","doi":"10.1007/s12021-024-09683-5","DOIUrl":"https://doi.org/10.1007/s12021-024-09683-5","url":null,"abstract":"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.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"9 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142182159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Neuroinformatics Pub Date : 2024-09-10 DOI: 10.1007/s12021-024-09690-6

Gabriel R. Palma, Conor Thornberry, Seán Commins, Rafael A. Moral

{"title":"Understanding Learning from EEG Data: Combining Machine Learning and Feature Engineering Based on Hidden Markov Models and Mixed Models","authors":"Gabriel R. Palma, Conor Thornberry, Seán Commins, Rafael A. Moral","doi":"10.1007/s12021-024-09690-6","DOIUrl":"https://doi.org/10.1007/s12021-024-09690-6","url":null,"abstract":"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.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"32 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142182162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Neuroinformatics Pub Date : 2024-07-01 Epub Date: 2024-06-11 DOI: 10.1007/s12021-024-09668-4

Harsh Sinha, Pradeep Reddy Raamana

{"title":"Solving the Pervasive Problem of Protocol Non-Compliance in MRI using an Open-Source tool mrQA.","authors":"Harsh Sinha, Pradeep Reddy Raamana","doi":"10.1007/s12021-024-09668-4","DOIUrl":"10.1007/s12021-024-09668-4","url":null,"abstract":"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.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"297-315"},"PeriodicalIF":2.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11329586/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141301974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Neuroinformatics 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

{"title":"Photogrammetry scans for neuroanatomy education - a new multi-camera system: technical note.","authors":"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","doi":"10.1007/s12021-024-09672-8","DOIUrl":"10.1007/s12021-024-09672-8","url":null,"abstract":"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.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"317-327"},"PeriodicalIF":2.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141312062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Comprehensive Connectivity Modeling. 实现全面的连接性建模。

IF 2.7 4区医学

Neuroinformatics Pub Date : 2024-07-01 DOI: 10.1007/s12021-024-09676-4

Campbell Coleman, John Darrell Van Horn

引用次数: 0

Predicting Cognitive Functioning for Patients with a High-Grade Glioma: Evaluating Different Representations of Tumor Location in a Common Space. 预测高级别胶质瘤患者的认知功能：评估共同空间中肿瘤位置的不同表征

IF 2.7 4区医学

Neuroinformatics Pub Date : 2024-07-01 Epub Date: 2024-06-20 DOI: 10.1007/s12021-024-09671-9

S M Boelders, W De Baene, E Postma, K Gehring, L L Ong

{"title":"Predicting Cognitive Functioning for Patients with a High-Grade Glioma: Evaluating Different Representations of Tumor Location in a Common Space.","authors":"S M Boelders, W De Baene, E Postma, K Gehring, L L Ong","doi":"10.1007/s12021-024-09671-9","DOIUrl":"10.1007/s12021-024-09671-9","url":null,"abstract":"Cognitive functioning is increasingly considered when making treatment decisions for patients with a brain tumor in view of a personalized onco-functional balance. Ideally, one can predict cognitive functioning of individual patients to make treatment decisions considering this balance. To make accurate predictions, an informative representation of tumor location is pivotal, yet comparisons of representations are lacking. Therefore, this study compares brain atlases and principal component analysis (PCA) to represent voxel-wise tumor location. Pre-operative cognitive functioning was predicted for 246 patients with a high-grade glioma across eight cognitive tests while using different representations of voxel-wise tumor location as predictors. Voxel-wise tumor location was represented using 13 different frequently-used population average atlases, 13 randomly generated atlases, and 13 representations based on PCA. ElasticNet predictions were compared between representations and against a model solely using tumor volume. Preoperative cognitive functioning could only partly be predicted from tumor location. Performances of different representations were largely similar. Population average atlases did not result in better predictions compared to random atlases. PCA-based representation did not clearly outperform other representations, although summary metrics indicated that PCA-based representations performed somewhat better in our sample. Representations with more regions or components resulted in less accurate predictions. Population average atlases possibly cannot distinguish between functionally distinct areas when applied to patients with a glioma. This stresses the need to develop and validate methods for individual parcellations in the presence of lesions. Future studies may test if the observed small advantage of PCA-based representations generalizes to other data.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"329-352"},"PeriodicalIF":2.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11329426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141428087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology. MRIO：磁共振成像获取与分析本体论。

IF 2.7 4区医学

Neuroinformatics Pub Date : 2024-07-01 Epub Date: 2024-05-20 DOI: 10.1007/s12021-024-09664-8

Alexander Bartnik, Lucas M Serra, Mackenzie Smith, William D Duncan, Lauren Wishnie, Alan Ruttenberg, Michael G Dwyer, Alexander D Diehl

{"title":"MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology.","authors":"Alexander Bartnik, Lucas M Serra, Mackenzie Smith, William D Duncan, Lauren Wishnie, Alan Ruttenberg, Michael G Dwyer, Alexander D Diehl","doi":"10.1007/s12021-024-09664-8","DOIUrl":"10.1007/s12021-024-09664-8","url":null,"abstract":"Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principles and has contributed several classes to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. MRIO addresses the need for a \"common language\" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"269-283"},"PeriodicalIF":2.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141066422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational Tools for Neuronal Morphometric Analysis: A Systematic Search and Review. 神经元形态计量分析的计算工具：系统搜索与回顾。

IF 2.7 4区医学

Neuroinformatics Pub Date : 2024-07-01 Epub Date: 2024-06-26 DOI: 10.1007/s12021-024-09674-6

Jéssica Leite, Fabiano Nhoatto, Antonio Jacob, Roberto Santana, Fábio Lobato

{"title":"Computational Tools for Neuronal Morphometric Analysis: A Systematic Search and Review.","authors":"Jéssica Leite, Fabiano Nhoatto, Antonio Jacob, Roberto Santana, Fábio Lobato","doi":"10.1007/s12021-024-09674-6","DOIUrl":"10.1007/s12021-024-09674-6","url":null,"abstract":"Morphometry is fundamental for studying and correlating neuronal morphology with brain functions. With increasing computational power, it is possible to extract morphometric characteristics automatically, including features such as length, volume, and number of neuron branches. However, to the best of our knowledge, there is no mapping of morphometric tools yet. In this context, we conducted a systematic search and review to identify and analyze tools within the scope of neuron analysis. Thus, the work followed a well-defined protocol and sought to answer the following research questions: What open-source tools are available for neuronal morphometric analysis? What morphometric characteristics are extracted by these tools? For this, aiming for greater robustness and coverage, the study was based on the paper analysis as well as the study of documentation and tests with the tools available in repositories. We analyzed 1,586 papers and mapped 23 tools, where NeuroM, L-Measure, and NeuroMorphoVis extract the most features. Furthermore, we contribute to the body of knowledge with the unprecedented presentation of 150 unique morphometric features whose terminologies were categorized and standardized. Overall, the study contributes to advancing the understanding of the complex mechanisms underlying the brain.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"353-377"},"PeriodicalIF":2.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141452047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying Diagnostic Biomarkers for Autism Spectrum Disorder From Higher-order Interactions Using the PED Algorithm. 利用 PED 算法从高阶相互作用中识别自闭症谱系障碍的诊断生物标志物

IF 2.7 4区医学

Neuroinformatics Pub Date : 2024-07-01 Epub Date: 2024-05-21 DOI: 10.1007/s12021-024-09662-w

Hao Wang, Yanting Liu, Yanrui Ding

{"title":"Identifying Diagnostic Biomarkers for Autism Spectrum Disorder From Higher-order Interactions Using the PED Algorithm.","authors":"Hao Wang, Yanting Liu, Yanrui Ding","doi":"10.1007/s12021-024-09662-w","DOIUrl":"10.1007/s12021-024-09662-w","url":null,"abstract":"In the field of neuroimaging, more studies of abnormalities in brain regions of the autism spectrum disorder (ASD) usually focused on two brain regions connected, and less on abnormalities of higher-order interactions of brain regions. To explore the complex relationships of brain regions, we used the partial entropy decomposition (PED) algorithm to capture higher-order interactions by computing the higher-order dependencies of all three brain regions (triads). We proposed a method for examining the effect of individual brain regions on triads based on the PED and surrogate tests. The key triads were discovered by analyzing the effects. Further, the hypergraph modularity maximization algorithm revealed the higher-order brain structures, of which the link between right thalamus and left thalamus in ASD was more loose compared with the typical control (TC). Redundant key triad (left cerebellum crus 1 and left precuneus and right inferior occipital gyrus) exhibited a discernible attenuation in interaction in ASD, while the synergistic key triad (right cerebellum crus 1 and left postcentral gyrus and left lingual gyrus) indicated a notable decline. The results of classification model further confirmed the potential of the key triads as diagnostic biomarkers.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"285-296"},"PeriodicalIF":2.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141072117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative Modelling of Cortical Receptor Distributions from Cytoarchitectonic Images in the Macaque Brain. 根据猕猴大脑的细胞结构图像建立皮质受体分布生成模型

IF 2.7 4区医学

Neuroinformatics Pub Date : 2024-07-01 Epub Date: 2024-07-08 DOI: 10.1007/s12021-024-09673-7

Ahmed Nebli, Christian Schiffer, Meiqi Niu, Nicola Palomero-Gallagher, Katrin Amunts, Timo Dickscheid

{"title":"Generative Modelling of Cortical Receptor Distributions from Cytoarchitectonic Images in the Macaque Brain.","authors":"Ahmed Nebli, Christian Schiffer, Meiqi Niu, Nicola Palomero-Gallagher, Katrin Amunts, Timo Dickscheid","doi":"10.1007/s12021-024-09673-7","DOIUrl":"10.1007/s12021-024-09673-7","url":null,"abstract":"Neurotransmitter receptor densities are relevant for understanding the molecular architecture of brain regions. Quantitative in vitro receptor autoradiography, has been introduced to map neurotransmitter receptor distributions of brain areas. However, it is very time and cost-intensive, which makes it challenging to obtain whole-brain distributions. At the same time, high-throughput light microscopy and 3D reconstructions have enabled high-resolution brain maps capturing measures of cell density across the whole human brain. Aiming to bridge gaps in receptor measurements for building detailed whole-brain atlases, we study the feasibility of predicting realistic neurotransmitter density distributions from cell-body stainings. Specifically, we utilize conditional Generative Adversarial Networks (cGANs) to predict the density distributions of the M2 receptor of acetylcholine and the kainate receptor for glutamate in the macaque monkey's primary visual (V1) and motor cortex (M1), based on light microscopic scans of cell-body stained sections. Our model is trained on corresponding patches from aligned consecutive sections that display cell-body and receptor distributions, ensuring a mapping between the two modalities. Evaluations of our cGANs, both qualitative and quantitative, show their capability to predict receptor densities from cell-body stained sections while maintaining cortical features such as laminar thickness and curvature. Our work underscores the feasibility of cross-modality image translation problems to address data gaps in multi-modal brain atlases.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"389-402"},"PeriodicalIF":2.7,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11329581/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141555809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0