2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)最新文献_第9页

Automatic detection of aortic dissection in contrast-enhanced CT 增强CT对主动脉夹层的自动检测

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950582

E. Dehghan, Hongzhi Wang, T. Syeda-Mahmood

引用次数: 20

Automated vesicle fusion detection using Convolutional Neural Networks 基于卷积神经网络的自动囊泡融合检测

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950497

Haohan Li, Zhaozheng Yin, Yingke Xu

引用次数: 2

A simple respiratory motion analysis method for chest tomosynthesis 一种简易胸腔断层合成呼吸运动分析方法

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950569

Hua Zhang, X. Tao, G. Qin, Jianhua Ma, Qianjin Feng, Wufan Chen

引用次数: 0

Enhancement of 250-MHz quantitative acoustic-microscopy data using a single-image super-resolution method 使用单图像超分辨率方法增强250-MHz定量声学显微镜数据

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950645

A. Basarab, D. Rohrbach, Ningning Zhao, J. Tourneret, D. Kouamé, J. Mamou

引用次数: 4

The structural disconnectome: A pathology-sensitive extension of the structural connectome 结构断连组:结构连接组的病理敏感延伸

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950539

C. Langen, M. Vernooij, L. Cremers, Wyke Huizinga, M. Groot, M. Ikram, T. White, W. Niessen

引用次数: 3

An easy-to-use image labeling platform for automatic magnetic resonance image quality assessment 一个易于使用的图像标记平台，用于自动磁共振图像质量评估

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950628

Thomas Kustner, Philipp Wolf, Martin Schwartz, Annika Liebgott, F. Schick, S. Gatidis, Bin Yang

引用次数: 2

Multi-angle TOF MR brain angiography of the common marmoset 普通狨猴的多角度TOF MR脑血管造影

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950714

M. Mescam, J. Brossard, N. Vayssiere, C. Fonta

引用次数: 2

HEp-2 cell classification based on a Deep Autoencoding-Classification convolutional neural network 基于深度自编码-分类卷积神经网络的HEp-2细胞分类

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950689

Jingxin Liu, Bolei Xu, L. Shen, J. Garibaldi, G. Qiu

引用次数: 15

Neuron reconstruction from fluorescence microscopy images using sequential Monte Carlo estimation 神经元重建从荧光显微镜图像使用顺序蒙特卡罗估计

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950462

M. Radojević, E. Meijering

{"title":"Neuron reconstruction from fluorescence microscopy images using sequential Monte Carlo estimation","authors":"M. Radojević, E. Meijering","doi":"10.1109/ISBI.2017.7950462","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950462","url":null,"abstract":"Microscopic analysis of neuronal cell morphology is required in many studies in neurobiology. The development of computational methods for this purpose is an ongoing challenge and includes solving some of the fundamental computer vision problems such as detecting and grouping sometimes very noisy line-like image structures. Advancements in the field are impeded by the complexity and immense diversity of neuronal cell shapes across species and brain regions, as well as by the high variability in image quality across labs and experimental setups. Here we present a novel method for fully automatic neuron reconstruction based on sequential Monte Carlo estimation. It uses newly designed models for predicting and updating branch node estimates as well as novel initialization and final tree construction strategies. The proposed method was evaluated on 3D fluorescence microscopy images containing single neurons and neuronal networks for which manual annotations were available as gold-standard references. The results indicate that our method performs favorably compared to state-of-the-art alternative methods.","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":"72954974","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: 6

Feature selection and thyroid nodule classification using transfer learning 基于迁移学习的特征选择和甲状腺结节分类

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-01 DOI: 10.1109/ISBI.2017.7950707

Tianjiao Liu, Shuaining Xie, Yukang Zhang, Jing Yu, Lijuan Niu, Weidong Sun

{"title":"Feature selection and thyroid nodule classification using transfer learning","authors":"Tianjiao Liu, Shuaining Xie, Yukang Zhang, Jing Yu, Lijuan Niu, Weidong Sun","doi":"10.1109/ISBI.2017.7950707","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950707","url":null,"abstract":"Ultrasonography is a valuable diagnosis method for thyroid nodules. Automatically discriminating benign and malignant nodules in the ultrasound images can provide aided diagnosis suggestions, or increase the diagnosis accuracy when lack of experts. The core problem in this issue is how to capture appropriate features for this specific task. Here, we propose a feature extraction method for ultrasound images based on the convolution neural networks (CNNs), try to introduce more meaningful and specific features to the classification. A CNN model trained with ImageNet data is transferred to the ultrasound image domain, to generate semantic deep features under small sample condition. Then, we combine those deep features with conventional features such as Histogram of Oriented Gradient (HOG) and Scale Invariant Feature Transform (SIFT) together to form a hybrid feature space. Furthermore, to make the general deep features more pertinent to our problem, a feature subset selection process is employed for the hybrid nodule classification, followed by a detailed discussion on the influence of feature number and feature composition method. Experimental results on 1037 images show that the accuracy of our proposed method is 0.929, which outperforms other relative methods by over 10%.","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":"72801861","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: 23