2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro最新文献_第9页

Computer-aided prognosis: Predicting patient and disease outcome via multi-modal image analysis 计算机辅助预后:通过多模态图像分析预测患者和疾病的预后

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490264

A. Madabhushi, A. Basavanhally, Scott Doyle, S. Agner, George Lee

{"title":"Computer-aided prognosis: Predicting patient and disease outcome via multi-modal image analysis","authors":"A. Madabhushi, A. Basavanhally, Scott Doyle, S. Agner, George Lee","doi":"10.1109/ISBI.2010.5490264","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490264","url":null,"abstract":"Computer-aided prognosis (CAP) is a new and exciting complement to the field of computer-aided diagnosis (CAD) and involves developing computerized image analysis and multi-modal data fusion algorithms for helping physicians predict disease outcome and patient survival. At the Laboratory for Computational Imaging and Bioinformatics (LCIB)1 at Rutgers University we have been developing computerized algorithms for high dimensional data and image analysis for predicting disease outcome from multiple modalities includng MRI, digital pathology, and protein expression. Additionally, we have been developing novel data fusion algorithms based on nonlinear dimensionality reduction methods (such as Graph Embedding) to quantitatively integrate prognostic information from multiple data sources and modalities. In this paper, we briefly describe 5 representative and ongoing CAP projects at LCIB. These projects include (1) an Image-based Risk Score (IbRiS) algorithm for predicting outcome of ER+ breast cancer patients based on quantitative image analysis of digitized breast cancer biopsy specimens alone, (2) segmenting and determining extent of lymphocytic infiltration (identified as a possible prognostic marker for outcome in Her2+ breast cancers) from digitized histopathology, (3) segmenting and diagnosing highly agressive triple-negative breast cancers on dynamic contrast enhanced (DCE) MRI, (4) distinguishing patients with different Gleason grades of prostate cancer (grade being known to be correlated to outcome) from digitzed needle biopsy specimens, and (5) integrating protein expression measurements obtained from mass spectrometry with quantitative image features derived from digitized histopathology for distinguishing between prostate cancer patients at low and high risk of disease recurrence.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124841417","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

A new framework for sparse regularization in limited angle x-ray tomography 有限角度x射线断层扫描稀疏正则化的新框架

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490113

J. Frikel

引用次数: 12

Comparison of magnetic resonance electrical impedance tomography (MREIT) reconstruction algorithms 磁共振电阻抗断层成像(MREIT)重建算法的比较

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490080

B. M. Eyüboğlu, V. Arpinar, R. Boyacioglu, E. Değirmenci, G. Eker

引用次数: 4

Multiphase level set for automated delineation of membrane-bound macromolecules 用于自动描述膜结合大分子的多相水平设置

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490389

Hang Chang, B. Parvin

引用次数: 8

Analytical form of Shepp-Logan phantom for parallel MRI 平行MRI的Shepp-Logan影的解析形式

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490365

M. Guerquin-Kern, F. I. Karahanoğlu, D. Ville, K. Pruessmann, M. Unser

引用次数: 7

3D shape context surface registration for cortical mapping 用于皮质映射的三维形状上下文表面配准

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490163

O. Acosta, J. Fripp, A. Rueda, D. Xiao, E. Bonner, P. Bourgeat, Olivier Salvado

{"title":"3D shape context surface registration for cortical mapping","authors":"O. Acosta, J. Fripp, A. Rueda, D. Xiao, E. Bonner, P. Bourgeat, Olivier Salvado","doi":"10.1109/ISBI.2010.5490163","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490163","url":null,"abstract":"Deformable registration of cortical surfaces facilitates longitudinal and intergroup comparisons of cortical structure and function in the study of many neurodegenerative diseases. Non-rigid cortical matching is a challenging task due to the large variability between individuals and the complexity of the cortex. We present a new framework for computing cortical correspondences on brain surfaces based on 3D Shape Context and mean curvatures of partially flattened surfaces (PFS). Our approach is scale invariant and provides an accurate and anatomically meaningful alignment across the population. Registering PFS, instead of original cortical surfaces, simplifies the determination of shape correspondences, overcoming the problem of intersubject variability, while still guaranteeing the alignment of the main brain lobes and folding patterns. We validated the approach using 30 segmented brains from the OASIS database registered to a common space and compared the results with Freesurfer. In average, mean absolute distance of 0.36 and Hausdorff distance of 5.06 between moving and target surfaces are obtained. Further localization of labelled areas on each hemisphere demonstrated the accuracy of the technique.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126379028","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: 13

Improving hard exudate detection in retinal images through a combination of local and contextual information 通过结合局部和上下文信息改进视网膜图像中的硬渗出物检测

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490429

C. I. Sánchez, M. Niemeijer, M. Suttorp-Schulten, M. Abràmoff, B. Ginneken

引用次数: 37

Robust guidewire segmentation through boosting, clustering and linear programming 通过增强、聚类和线性规划实现鲁棒导丝分割

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490138

N. Honnorat, Régis Vaillant, N. Paragios

引用次数: 15

Vesselness-guided variational segmentation of cellular networks from 3D micro-CT 血管引导的三维微ct细胞网络变分分割

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490135

A. Pacureanu, C. Revol-Muller, J. Rose, Maria Sanchez Ruiz, F. Peyrin

引用次数: 18

Evaluation of effects of JPEG2000 compression on a computer-aided detection system for prostate cancer on digitized histopathology JPEG2000压缩对前列腺癌计算机辅助检测系统数字化组织病理学的影响评价

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro Pub Date : 2010-04-14 DOI: 10.1109/ISBI.2010.5490238

Scott Doyle, J. Monaco, A. Madabhushi, S. Lindholm, P. Ljung, Lance Ladic, J. Tomaszeweski, M. Feldman

{"title":"Evaluation of effects of JPEG2000 compression on a computer-aided detection system for prostate cancer on digitized histopathology","authors":"Scott Doyle, J. Monaco, A. Madabhushi, S. Lindholm, P. Ljung, Lance Ladic, J. Tomaszeweski, M. Feldman","doi":"10.1109/ISBI.2010.5490238","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490238","url":null,"abstract":"A single digital pathology image can occupy over 10 gigabytes of hard disk space, rendering it difficult to store, analyze, and transmit. Though image compression provides a means of reducing the storage requirement, its effects on CAD (and pathologist) performance are not yet clear. In this work we assess the impact of compression on the ability of a CAD system to detect carcinoma of the prostate (CaP) in histological sections. The CAD algorithm proceeds as follows: Glands in the tissue are segmented using a region-growing algorithm. The size of each gland is then extracted and modeled using a mixture of Gamma distributions. A Markov prior (specifically, a probabilistic pairwise Markov model) is employed to encourage nearby glands to share the same class (i.e. cancerous or non-cancerous). Finally, cancerous glands are aggregated into continuous regions using a distance-hull algorithm. We evaluate CAD performance over 12 images compressed at 14 different compression ratios using JPEG2000. Algorithm performance (measured using the under the receiver operating characteristic curves) remains relatively constant for compression ratios up to 1:256. After this point performance degrades precipitously. We also have an expert pathologist view the compressed images and assign a confidence measure as to their diagnostic fidelity.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"306 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121828436","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: 13