2013 International Workshop on Pattern Recognition in Neuroimaging最新文献_第3页

Fast Clustering for Interactive Tractography Segmentation 交互式轨迹图分割的快速聚类

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.20

E. Olivetti, Thien Bao Nguyen, E. Garyfallidis, Nivedita Agarwal, P. Avesani

{"title":"Fast Clustering for Interactive Tractography Segmentation","authors":"E. Olivetti, Thien Bao Nguyen, E. Garyfallidis, Nivedita Agarwal, P. Avesani","doi":"10.1109/PRNI.2013.20","DOIUrl":"https://doi.org/10.1109/PRNI.2013.20","url":null,"abstract":"We developed a novel interactive system for human brain tractography segmentation to assist neuroanatomists in identifying white matter anatomical structures of interest from diffusion magnetic resonance imaging (dMRI) data. The difficulty in segmenting and navigating tractographies lies in the very large number of reconstructed neuronal pathways, i.e. the streamlines, which are in the order of hundreds of thousands with modern dMRI techniques. The novelty of our system resides in presenting the user a clustered version of the tractography in which she selects some of the clusters to identify a superset of the streamlines of interest. This superset is then re-clustered at a finer scale and again the user is requested to select the relevant clusters. The process of re-clustering and manual selection is iterated until the remaining streamlines faithfully represent the desired anatomical structure of interest. In this work we present a solution to solve the computational issue of clustering a large number of streamlines under the strict time constraints requested by the interactive use. The solution consists in embedding the streamlines into a Euclidean space and then in adopting a state-of-the art scalable implementation of the k-means algorithm. We tested the proposed system on tractographies from amyotrophic lateral sclerosis (ALS) patients and healthy subjects that we collected for a forthcoming study about the systematic differences between their corticospinal tracts.","PeriodicalId":144007,"journal":{"name":"2013 International Workshop on Pattern Recognition in Neuroimaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116106408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Classification of Structured EEG Tensors Using Nuclear Norm Regularization: Improving P300 Classification 基于核范数正则化的结构化脑电张量分类:改进P300分类

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.34

B. Hunyadi, Marco Signoretto, S. Debener, S. Huffel, M. Vos

引用次数: 6

Comparing Structural Brain Connectivity by the Infinite Relational Model 用无限关系模型比较脑结构连通性

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.22

Karen Sandø Ambrosen, Tue Herlau, T. Dyrby, Mikkel N. Schmidt, Morten Mørup

引用次数: 17

Wrapper Methods to Correct Mislabelled Training Data 纠正错误标记训练数据的包装方法

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.51

Jonathan Young, J. Ashburner, S. Ourselin

{"title":"Wrapper Methods to Correct Mislabelled Training Data","authors":"Jonathan Young, J. Ashburner, S. Ourselin","doi":"10.1109/PRNI.2013.51","DOIUrl":"https://doi.org/10.1109/PRNI.2013.51","url":null,"abstract":"Machine learning has obvious applications to the diagnosis of disease, and for many neurological conditions features extracted from brain images allow classifiers based on neuroimaging biomarkers to provide a useful complement to more traditional diagnostic methods based on symptoms and psychological testing. However the labels used in the training of such systems frequently depend on standard clinical diagnostic methods, meaning they are not completely reliable in many cases. This uncertainty makes the problems this causes hard to study, as it is difficult to measure both the extent of mislabelling and its effect on results. To avoid this problem, we perform classification of gender based on imaging, as this is definitely known for each subject. We then deliberately make known proportions of the training labels incorrect. This allows us to assess the effect of the level of label noise on classification accuracy, and evaluate methods that allow for the mislabelled data. The methods are wrappers using existing well known classifier algorithms. The results indicate that the methods can be significantly effective at realistic levels of noise in the training labels, but care must be taken in choosing which method to apply depending on the level of label noise.","PeriodicalId":144007,"journal":{"name":"2013 International Workshop on Pattern Recognition in Neuroimaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130549471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Automatic White Matter Fiber Clustering Using Dominant Sets 基于优势集的白质纤维自动聚类

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.62

Luca Dodero, Sebastiano Vascon, L. Giancardo, A. Gozzi, Diego Sona, Vittorio Murino

引用次数: 9

Sparse Source EEG Imaging with the Variational Garrote 稀疏源脑电图变喉成像

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.36

Sofie Therese Hansen, Carsten Stahlhut, L. K. Hansen

引用次数: 7

Gender-Specific Effects of Health and Lifestyle Markers on Individual BrainAGE 健康和生活方式标记对个体脑年龄的性别特异性影响

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.33

K. Franke, M. Ristow, Christian Gaser

引用次数: 6

Semi-spatiotemporal fMRI Brain Decoding 半时空功能磁共振大脑解码

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.54

M. H. Kefayati, H. Sheikhzadeh, H. Rabiee, A. Soltani-Farani

引用次数: 4

Detection of Transient Inter-regional Coupling in fMRI Time Series: A New Method Combining Inter-subjects Synchronization and Cluster-Analyses fMRI时间序列中瞬态区域间耦合检测:一种结合主体间同步和聚类分析的新方法

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.39

Cécile Bordier, E. Macaluso

引用次数: 1

A Comparison of Metrics and Algorithms for Fiber Clustering 光纤聚类的度量和算法比较

2013 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2013-06-22 DOI: 10.1109/PRNI.2013.56

Viviana Siless, Sergio Medina, G. Varoquaux, B. Thirion

引用次数: 17