2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)最新文献_第8页

A Convolutional Framework for Forward and Back-Projection in Fan-Beam Geometry 扇形波束几何中正反投影的卷积框架

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-08 DOI: 10.1109/ISBI.2019.8759285

Kai Zhang, A. Entezari

引用次数: 0

Curve Fitting Criteria to Determine Arterial Input Function for MR Perfusion Analysis 曲线拟合标准确定动脉输入函数的MR灌注分析

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-08 DOI: 10.1109/ISBI.2019.8759307

A. Huang, Chung-wei Lee, Hon-Man Liu

引用次数: 2

Optimized Multi-Atlas Prostate Segmentation From 3D CT Images 基于3D CT图像的优化多图谱前列腺分割

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-08 DOI: 10.1109/ISBI.2019.8759389

Yitian Zhou, L. Launay, J. Bert, R. Crevoisier, O. Acosta

引用次数: 0

Context-Refined Neural Cell Instance Segmentation 上下文精细神经细胞实例分割

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-08 DOI: 10.1109/ISBI.2019.8759204

Jingru Yi, Pengxiang Wu, Qiaoying Huang, Hui Qu, D. Hoeppner, Dimitris N. Metaxas

{"title":"Context-Refined Neural Cell Instance Segmentation","authors":"Jingru Yi, Pengxiang Wu, Qiaoying Huang, Hui Qu, D. Hoeppner, Dimitris N. Metaxas","doi":"10.1109/ISBI.2019.8759204","DOIUrl":"https://doi.org/10.1109/ISBI.2019.8759204","url":null,"abstract":"Neural cell instance segmentation serves as a valuable tool for the study of neural cell behaviors. In general, the instance segmentation methods compute the region of interest (ROI) through a detection module, where the segmentation is subsequently performed. To precisely segment the neural cells, especially their tiny and slender structures, existing work employs a u-net structure to preserve the low-level details and encode the high-level semantics. However, such method is insufficient for differentiating the adjacent cells when large parts of them are included in the same cropped ROI. To solve this problem, we propose a context-refined neural cell instance segmentation model that learns to suppress the background information. In particular, we employ a light-weight context refinement module to recalibrate the deep features and focus the model exclusively on the target cell within each cropped ROI. The proposed model is efficient and accurate, and experimental results demonstrate its superiority compared to the state-of-the-arts. Code is available at https://github.com/yijingru/CRNCIS-Pytorch.","PeriodicalId":119935,"journal":{"name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130854941","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: 7

Deep convolutional encoder-decoders for deltoid segmentation using healthy versus pathological learning transferability 基于健康与病理学习可转移性的三角肌分割深度卷积编码器

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-08 DOI: 10.1109/ISBI.2019.8759378

Pierre-Henri Conze, C. Pons, V. Burdin, F. Sheehan, S. Brochard

引用次数: 8

A Saliency-Aware Hybrid Dense Network for Bleeding Detection in Wireless Capsule Endoscopy Images 无线胶囊内窥镜图像出血检测的显著性感知混合密集网络

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-08 DOI: 10.1109/ISBI.2019.8759401

Xiaohan Xing, Yixuan Yuan, Xiao Jia, M. Meng

引用次数: 8

Sparse Low-rank Constrained Adaptive Structure Learning using Multi-template for Autism Spectrum Disorder Diagnosis 基于多模板的稀疏低秩约束自适应结构学习用于自闭症谱系障碍诊断

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-01 DOI: 10.1109/ISBI.2019.8759487

Fanglin Huang, A. El-Azab, Le Ou-Yang, Joseph Tan, Tianfu Wang, Baiying Lei

{"title":"Sparse Low-rank Constrained Adaptive Structure Learning using Multi-template for Autism Spectrum Disorder Diagnosis","authors":"Fanglin Huang, A. El-Azab, Le Ou-Yang, Joseph Tan, Tianfu Wang, Baiying Lei","doi":"10.1109/ISBI.2019.8759487","DOIUrl":"https://doi.org/10.1109/ISBI.2019.8759487","url":null,"abstract":"Autism spectrum disorder (ASD) is a developmental disability that causes severe social, communication and behavioral challenges. Up to now, many imaging-based approaches for ASD diagnosis have been proposed. However most of them limited to single template. In this paper, we propose a novel sparse low-rank constrained multi-templates data based method for ASD diagnosis, which performs feature selection and adaptive local structure learning simultaneously. Specifically, we encode modularity prior while constructing functional connectivity (FC) brain networks from different templates for each subject. After extracting features from FC networks, feature selection is applied. Meanwhile, the local structure is learnt via an adaptive process. Extensive experiments are conducted to demonstrate the effectiveness of our proposed method on the Autism Brain Imaging Data Exchange (ABIDE) database. Experimental results verify our proposed method can enhance the diagnosis performances and outperform the commonly used and state-of-the-art methods.","PeriodicalId":119935,"journal":{"name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115638329","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: 5

Unsupervised Deep Novelty Detection: Application To Muscle Ultrasound And Myositis Screening 无监督深度新颖检测:在肌肉超声和肌炎筛查中的应用

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-01 DOI: 10.1109/ISBI.2019.8759565

P. Burlina, Neil J. Joshi, Seth D. Billings, I-J. Wang, J. Albayda

引用次数: 4

A comparison between image and signal sharpness-based axial localization of ultrasound scatterers 基于图像和信号清晰度的超声散射体轴向定位的比较

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-01 DOI: 10.1109/ISBI.2019.8759225

K. Diamantis, P. Dalgarno, T. Anderson, J. Jensen, V. Sboros

{"title":"A comparison between image and signal sharpness-based axial localization of ultrasound scatterers","authors":"K. Diamantis, P. Dalgarno, T. Anderson, J. Jensen, V. Sboros","doi":"10.1109/ISBI.2019.8759225","DOIUrl":"https://doi.org/10.1109/ISBI.2019.8759225","url":null,"abstract":"Super-resolution ultrasound imaging deploys contrast microbubble (MB) tracking to delineate micro-vessels. The potential application spans to a large number of diseases which cause compromised vascular networks. Current super-resolution methods are mainly based on image processing. Sharpness-based localization is an alternative to such methods for scatterer localization in the axial direction, and can be implemented using both image and signal data. A 7-MHz, linear ultrasound transducer $(lambda = 212 mu mathrm{m})$ and the Synthetic Aperture Real-time Ultrasound System (SARUS) were used to image a wire-target (point scatterer) at different depth positions. The method predicts a depth estimate and its difference from the true scatterer position demonstrates its accuracy. This average difference can be as low as $27.41 mu mathrm{m}($ or $approx lambda /8)$ for the image-derived sharpness and drops to $2.84 mu mathrm{m}($ or $approx quad lambda /75)$ when the signals are used. These figures were calculated for a 8 mm depth range, which can be extended subject to further processing. The process of image formation involves interpolation and logarithmic compression that reduce the overall performance of the method when using image data. Such details may be significant when reconstructing micro-vessels of the order of tens of micrometres in diameter.","PeriodicalId":119935,"journal":{"name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117242710","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: 1

Hierarchical Joint Registration of Tissue Blocks With Soft Shape Constraints For Large-Scale Histology of The Human Brain 具有软形状约束的组织块分层联合配准用于人脑大尺度组织学

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Pub Date : 2019-04-01 DOI: 10.1109/ISBI.2019.8759396

M. Mancini, Shauna Crampsie, David L. Thomas, Z. Jaunmuktane, J. Holton, J. E. Iglesias

{"title":"Hierarchical Joint Registration of Tissue Blocks With Soft Shape Constraints For Large-Scale Histology of The Human Brain","authors":"M. Mancini, Shauna Crampsie, David L. Thomas, Z. Jaunmuktane, J. Holton, J. E. Iglesias","doi":"10.1109/ISBI.2019.8759396","DOIUrl":"https://doi.org/10.1109/ISBI.2019.8759396","url":null,"abstract":"Large-scale 3D histology reconstruction of the human brain with MRI as volumetric reference generally requires reassembling the tissue blocks into the MRI space, prior to any further reconstruction of the histology of the individual blocks. This is a challenging registration problem, particularly in the frequent case that blockface photographs of paraffin embedded tissue are used as intermediate modality, as their contrast between white and gray matter is rather low. Here we propose a registration framework to address this problem, relying on two key components. First, blocks are simultaneously aligned to the MRI while exploiting the spatial constraints that they impose on each other, by means of a customized soft shape constraint (similarly to a jigsaw puzzle). And second, we adopt a hierarchical optimization strategy that capitalizes on our prior knowledge on the slicing and blocking procedure. Our framework is validated quantitatively on synthetic data, and qualitatively on the histology of a whole human hemisphere.","PeriodicalId":119935,"journal":{"name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121254093","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: 2