2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)最新文献_第8页

Fitting networks models for functional brain connectivity 拟合大脑功能连接的网络模型

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950573

Jagath Rajapakse, Sukrit Gupta, Xiuchao Sui

引用次数: 7

Automated multi-stage segmentation of white blood cells via optimizing color processing 通过优化颜色处理实现白细胞的自动多阶段分割

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950584

Afaf Tareef, Yang Song, D. Feng, Mei Chen, Weidong (Tom) Cai

引用次数: 24

Brain geometry persistent homology marker for Parkinson's disease 帕金森病的脑几何持久同源标记

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950575

Amanmeet Garg, Donghuan Lu, K. Popuri, M. Beg

引用次数: 6

Enhancing Bayesian PET image reconstruction using neural networks 利用神经网络增强贝叶斯PET图像重建

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950727

Bao Yang, L. Ying, Jing Tang

{"title":"Enhancing Bayesian PET image reconstruction using neural networks","authors":"Bao Yang, L. Ying, Jing Tang","doi":"10.1109/ISBI.2017.7950727","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950727","url":null,"abstract":"The performance of smoothness-enforced Bayesian PET image reconstruction is strongly affected by the weight on regularization. Compromises need to be made between variance and spatial resolution. In this work, we propose to use an artificial neural network (ANN) to fuse the image versions reconstructed from a maximum a posteriori (MAP) algorithm with different regularizing weights for quantitative improvement. Using the BrainWeb phantoms, we simulated PET data at different count levels for different subjects with and without lesions. We designed an ANN and trained it using MAP reconstructions with different regularization parameters for one normal subject at a specific count level. Reconstructed images from the simulations with lesions, of other count levels, and of other subjects were fused using the trained ANN. In all of the testing experiments, the designed ANN fusion keeps the noise level as low as what the MAP algorithm achieves at heavy regularization while significantly reduces the bias or improves the lesion contrast. We conclude that the proposed ANN fusion method removes the need for tuning the regularization of the MAP algorithm.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77626514","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: 3

A synthesis-based approach to compressive multi-contrast magnetic resonance imaging 一种基于综合的压缩多对比磁共振成像方法

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950615

Alper Gungor, E. Kopanoglu, T. Çukur, H. Guven

引用次数: 0

Disease grading of heterogeneous tissue using convolutional autoencoder 基于卷积自编码器的非均匀组织疾病分级

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950591

Erwan Zerhouni, B. Prisacari, Qing Zhong, P. Wild, M. Gabrani

引用次数: 1

Disentangling inter-subject variations: Automatic localization of ventricular tachycardia origin from 12-lead electrocardiograms 解开受试者间变异:从12导联心电图自动定位室性心动过速起源

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950596

Shuhang Chen, P. Gyawali, Huafeng Liu, B. Horáček, J. Sapp, Linwei Wang

{"title":"Disentangling inter-subject variations: Automatic localization of ventricular tachycardia origin from 12-lead electrocardiograms","authors":"Shuhang Chen, P. Gyawali, Huafeng Liu, B. Horáček, J. Sapp, Linwei Wang","doi":"10.1109/ISBI.2017.7950596","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950596","url":null,"abstract":"An automatic, real-time localization of ventricular tachycardia (VT) can improve the efficiency and efficacy of interventional therapies. Because the exit site of VT gives rise to its QRS morphology on electrocardiograms (ECG), it has been shown feasible to predict VT exits from 12-lead ECGs. However, existing work have reported limited resolution and accuracy due to a critical challenge: the significant inter-subject heterogeneity in ECG data. In this paper, we present a method to explicitly separate and represent the factors of variation in data throughout a deep network using denoising autoencoder with contrastive regularization. We demonstrate the performance of this method on an ECG dataset collected from 39 patients and 1012 distinct sites of ventricular origins. An improvement in the accuracy of localizing the origin of activation is obtained in comparison to a traditional approach that uses prescribed QRS features for prediction, as well as the use of a standard autoencoder network without separating the factors of variations in ECG data.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77000639","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: 9

Joint detection and clinical score prediction in Parkinson's disease via multi-modal sparse learning 基于多模态稀疏学习的帕金森病联合检测及临床评分预测

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950739

Haijun Lei, Jian Zhang, Zhang Yang, Ee-Leng Tan, B. Lei, Qiuming Luo

引用次数: 37

Nuclei segmentation in histopathology images using deep neural networks 基于深度神经网络的组织病理图像核分割

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950669

Peter Naylor, M. Laé, F. Reyal, Thomas Walter

引用次数: 156

Prostate segmentation in MR images using ensemble deep convolutional neural networks 基于集成深度卷积神经网络的磁共振图像前列腺分割

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950630

Haozhe Jia, Yong Xia, Weidong (Tom) Cai, M. Fulham, D. Feng

引用次数: 19