Medical computer vision and Bayesian and graphical models for biomedical imaging : MICCAI 2016 international workshop, MCV and BAMBI, Athens, Greece, October 21, 2016 : revised selected papers最新文献

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Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study. 用无监督纹理原型解释放射性肺气肿亚型:MESA COPD研究。

Medical computer vision and Bayesian and graphical models for biomedical imaging : MICCAI 2016 international workshop, MCV and BAMBI, Athens, Greece, October 21, 2016 : revised selected papers Pub Date : 2017-01-01 Epub Date: 2017-07-01 DOI: 10.1007/978-3-319-61188-4_7

Jie Yang, Elsa D Angelini, Benjamin M Smith, John H M Austin, Eric A Hoffman, David A Bluemke, R Graham Barr, Andrew F Laine

{"title":"Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study.","authors":"Jie Yang, Elsa D Angelini, Benjamin M Smith, John H M Austin, Eric A Hoffman, David A Bluemke, R Graham Barr, Andrew F Laine","doi":"10.1007/978-3-319-61188-4_7","DOIUrl":"10.1007/978-3-319-61188-4_7","url":null,"abstract":"Pulmonary emphysema is traditionally subcategorized into three subtypes, which have distinct radiological appearances on computed tomography (CT) and can help with the diagnosis of chronic obstructive pulmonary disease (COPD). Automated texture-based quantification of emphysema subtypes has been successfully implemented via supervised learning of these three emphysema subtypes. In this work, we demonstrate that unsupervised learning on a large heterogeneous database of CT scans can generate texture prototypes that are visually homogeneous and distinct, reproducible across subjects, and capable of predicting accurately the three standard radiological subtypes. These texture prototypes enable automated labeling of lung volumes, and open the way to new interpretations of lung CT scans with finer subtyping of emphysema.","PeriodicalId":92100,"journal":{"name":"Medical computer vision and Bayesian and graphical models for biomedical imaging : MICCAI 2016 international workshop, MCV and BAMBI, Athens, Greece, October 21, 2016 : revised selected papers","volume":"2017 ","pages":"69-80"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5708576/pdf/nihms858897.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35217343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LATEST: Local AdapTivE and Sequential Training for Tissue Segmentation of Isointense Infant Brain MR Images. LATEST：局部 AdapTivE 和序列训练用于等密度婴儿脑磁共振图像的组织分割。

Li Wang, Yaozong Gao, Gang Li, Feng Shi, Weili Lin, Dinggang Shen

{"title":"LATEST: Local AdapTivE and Sequential Training for Tissue Segmentation of Isointense Infant Brain MR Images.","authors":"Li Wang, Yaozong Gao, Gang Li, Feng Shi, Weili Lin, Dinggang Shen","doi":"10.1007/978-3-319-61188-4_3","DOIUrl":"10.1007/978-3-319-61188-4_3","url":null,"abstract":"Accurate segmentation of isointense infant (~6 months of age) brain MRIs is of great importance, however, a very challenging task, due to extremely low tissue contrast caused by ongoing myelination processes. In this work, we propose a novel learning method based on Local AdapTivE and Sequential Training (LATEST) for segmentation. Specifically, random forest technique is employed to train a local classifier (a single decision tree) for each voxel in the common space based on the neighboring training samples from atlases. Then, for each given voxel, all trained nearby individual classifiers (decision trees) are grouped together to form a forest. Moreover, the estimated probabilities are further used as additional source images to train the next set of local classifiers for refining tissue classification. By iteratively training the subsequent classifiers based on the updated tissue probability maps, a sequence of local classifiers can be built for accurate tissue segmentation.","PeriodicalId":92100,"journal":{"name":"Medical computer vision and Bayesian and graphical models for biomedical imaging : MICCAI 2016 international workshop, MCV and BAMBI, Athens, Greece, October 21, 2016 : revised selected papers","volume":"2017 ","pages":"26-34"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5705093/pdf/nihms849612.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35217344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Landmark-Based Alzheimer's Disease Diagnosis Using Longitudinal Structural MR Images. 基于地标的阿尔茨海默病纵向结构磁共振图像诊断。

Medical computer vision and Bayesian and graphical models for biomedical imaging : MICCAI 2016 international workshop, MCV and BAMBI, Athens, Greece, October 21, 2016 : revised selected papers Pub Date : 2016-10-01 Epub Date: 2017-07-01 DOI: 10.1007/978-3-319-61188-4_4

Jun Zhang, Mingxia Liu, Le An, Yaozong Gao, Dinggang Shen

{"title":"Landmark-Based Alzheimer's Disease Diagnosis Using Longitudinal Structural MR Images.","authors":"Jun Zhang, Mingxia Liu, Le An, Yaozong Gao, Dinggang Shen","doi":"10.1007/978-3-319-61188-4_4","DOIUrl":"10.1007/978-3-319-61188-4_4","url":null,"abstract":"In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which requires no nonlinear registration or tissue segmentation in the application stage and is robust to the inconsistency among longitudinal scans. Specifically, (1) the discriminative landmarks are first automatically discovered from the whole brain, which can be efficiently localized using a fast landmark detection method for the testing images; (2) High-level statistical spatial features and contextual longitudinal features are then extracted based on those detected landmarks. Using the spatial and longitudinal features, a linear support vector machine (SVM) is adopted for distinguishing AD subjects from healthy controls (HCs) and also mild cognitive impairment (MCI) subjects from HCs, respectively. Experimental results demonstrate the competitive classification accuracies, as well as a promising computational efficiency.","PeriodicalId":92100,"journal":{"name":"Medical computer vision and Bayesian and graphical models for biomedical imaging : MICCAI 2016 international workshop, MCV and BAMBI, Athens, Greece, October 21, 2016 : revised selected papers","volume":"10081 ","pages":"35-45"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-61188-4_4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35377862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12