Medical computer vision : large data in medical imaging : third international MICCAI workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : revised selected papers. MCV (Workshop) (3rd : 2013 : Nagoya-shi, Japan)最新文献_第2页

Medical computer vision : large data in medical imaging : third international MICCAI workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : revised selected papers. MCV (Workshop) (3rd : 2013 : Nagoya-shi, Japan) Pub Date : 2014-09-18 DOI: 10.1007/978-3-319-13972-2_6

E. Stühler, M. Berthold

引用次数: 1

Automatic Liver Segmentation Using Statistical Prior Models and Free-form Deformation 使用统计先验模型和自由形式变形的自动肝脏分割

Medical computer vision : large data in medical imaging : third international MICCAI workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : revised selected papers. MCV (Workshop) (3rd : 2013 : Nagoya-shi, Japan) Pub Date : 2014-09-18 DOI: 10.1007/978-3-319-13972-2_17

Xuhui Li, Cheng Huang, F. Jia, Zongmin Li, C. Fang, Yingfang Fan

引用次数: 23

Classifier-Based Multi-atlas Label Propagation with Test-Specific Atlas Weighting for Correspondence-Free Scenarios 基于分类器的多地图集标签传播与测试特定的地图集加权

Medical computer vision : large data in medical imaging : third international MICCAI workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : revised selected papers. MCV (Workshop) (3rd : 2013 : Nagoya-shi, Japan) Pub Date : 2014-09-18 DOI: 10.1007/978-3-319-13972-2_11

D. Zikic, Ben Glocker, A. Criminisi

引用次数: 6

Fast Multiatlas Selection Using Composition of Transformations for Radiation Therapy Planning 利用变换组合进行放射治疗计划的快速多图谱选择

Medical computer vision : large data in medical imaging : third international MICCAI workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : revised selected papers. MCV (Workshop) (3rd : 2013 : Nagoya-shi, Japan) Pub Date : 2014-09-18 DOI: 10.1007/978-3-319-13972-2_10

David Rivest-Hénault, S. Ghose, J. Pluim, P. Greer, J. Fripp, J. Dowling

引用次数: 2

Pectoralis Muscle Segmentation on CT Images Based on Bayesian Graph Cuts with a Subject-Tailored Atlas 基于主题定制图谱的贝叶斯图切割CT图像胸肌分割

Medical computer vision : large data in medical imaging : third international MICCAI workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : revised selected papers. MCV (Workshop) (3rd : 2013 : Nagoya-shi, Japan) Pub Date : 2014-09-18 DOI: 10.1007/978-3-319-13972-2_4

R. Harmouche, J. Ross, G. Washko, Raúl San José Estépar

引用次数: 5

Rule-Based Ventral Cavity Multi-organ Automatic Segmentation in CT Scans 基于规则的腹腔CT多器官自动分割

Medical computer vision : large data in medical imaging : third international MICCAI workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : revised selected papers. MCV (Workshop) (3rd : 2013 : Nagoya-shi, Japan) Pub Date : 2014-09-18 DOI: 10.1007/978-3-319-13972-2_15

Assaf B. Spanier, Leo Joskowicz

引用次数: 12

Organ Localization Using Joint AP/LAT View Landmark Consensus Detection and Hierarchical Active Appearance Models. 基于联合AP/LAT视图的器官定位地标一致性检测和分层活动外观模型。

Medical computer vision : large data in medical imaging : third international MICCAI workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : revised selected papers. MCV (Workshop) (3rd : 2013 : Nagoya-shi, Japan) Pub Date : 2014-01-01 Epub Date: 2014-04-01 DOI: 10.1007/978-3-319-05530-5_14

Qi Song, Albert Montillo, Roshni Bhagalia, V Srikrishnan

{"title":"Organ Localization Using Joint AP/LAT View Landmark Consensus Detection and Hierarchical Active Appearance Models.","authors":"Qi Song, Albert Montillo, Roshni Bhagalia, V Srikrishnan","doi":"10.1007/978-3-319-05530-5_14","DOIUrl":"https://doi.org/10.1007/978-3-319-05530-5_14","url":null,"abstract":"Parsing 2D radiographs into anatomical regions is a challenging task with many applications. In the clinic, scans routinely include anterior-posterior (AP) and lateral (LAT) view radiographs. Since these orthogonal views provide complementary anatomic information, an integrated analysis can afford the greatest localization accuracy. To solve this integration we propose automatic landmark candidate detection, pruned by a learned geometric consensus detector model and refined by fitting a hierarchical active appearance organ model (H-AAM). Our main contribution is twofold. First, we propose a probabilistic joint consensus detection model which learns how landmarks in either or both views predict landmark locations in a given view. Second, we refine landmarks by fitting a joint H-AAM that learns how landmark arrangement and image appearance can help predict across views. This increases accuracy and robustness to anatomic variation. All steps require just seconds to compute and compared to processing the scouts separately, joint processing reduces mean landmark distance error from 27.3 mm to 15.7 mm in LAT view and from 12.7 mm to 11.2 mm in the AP view. The errors are comparable to human expert inter-observer variability and suitable for clinical applications such as personalized scan planning for dose reduction. We assess our method using a database of scout CT scans from 93 subjects with widely varying pathology.","PeriodicalId":92822,"journal":{"name":"Medical computer vision : large data in medical imaging : third international MICCAI workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : revised selected papers. MCV (Workshop) (3rd : 2013 : Nagoya-shi, Japan)","volume":"8331 ","pages":"138-147"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6947663/pdf/nihms-1063776.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37523369","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: 5

Accurate Whole-Brain Segmentation for Alzheimer's Disease Combining an Adaptive Statistical Atlas and Multi-atlas. 结合自适应统计图谱和多图谱对阿尔茨海默病进行精确的全脑分割。

Medical computer vision : large data in medical imaging : third international MICCAI workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : revised selected papers. MCV (Workshop) (3rd : 2013 : Nagoya-shi, Japan) Pub Date : 2014-01-01 Epub Date: 2014-04-01 DOI: 10.1007/978-3-319-05530-5_7

Zhennan Yan, Shaoting Zhang, Xiaofeng Liu, Dimitris N Metaxas, Albert Montillo

{"title":"Accurate Whole-Brain Segmentation for Alzheimer's Disease Combining an Adaptive Statistical Atlas and Multi-atlas.","authors":"Zhennan Yan, Shaoting Zhang, Xiaofeng Liu, Dimitris N Metaxas, Albert Montillo","doi":"10.1007/978-3-319-05530-5_7","DOIUrl":"https://doi.org/10.1007/978-3-319-05530-5_7","url":null,"abstract":"Accurate segmentation of whole brain MR images including the cortex, white matter and subcortical structures is challenging due to inter-subject variability and the complex geometry of brain anatomy. However a precise solution would enable accurate, objective measurement of structure volumes for disease quantification. Our contribution is three-fold. First we construct an adaptive statistical atlas that combines structure specific relaxation and spatially varying adaptivity. Second we integrate an isotropic pairwise class-specific MRF model of label connectivity. Together these permit precise control over adaptivity, allowing many structures to be segmented simultaneously with superior accuracy. Third, we develop a framework combining the improved adaptive statistical atlas with a multi-atlas method which achieves simultaneous accurate segmentation of the cortex, ventricles, and sub-cortical structures in severely diseased brains, a feat not attained in [18]. We test the proposed method on 46 brains including 28 diseased brain with Alzheimer's and 18 healthy brains. Our proposed method yields higher accuracy than state-of-the-art approaches on both healthy and diseased brains.","PeriodicalId":92822,"journal":{"name":"Medical computer vision : large data in medical imaging : third international MICCAI workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : revised selected papers. MCV (Workshop) (3rd : 2013 : Nagoya-shi, Japan)","volume":"8331 ","pages":"65-73"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-05530-5_7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49686118","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: 9

Multi-structure Atlas-Based Segmentation Using Anatomical Regions of Interest 基于多结构图谱的感兴趣解剖区域分割

Medical computer vision : large data in medical imaging : third international MICCAI workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : revised selected papers. MCV (Workshop) (3rd : 2013 : Nagoya-shi, Japan) Pub Date : 2013-09-26 DOI: 10.1007/978-3-319-05530-5_21

O. J. D. Toro, H. Müller

引用次数: 11

Automatic Aorta Detection in Non-contrast 3D Cardiac CT Images Using Bayesian Tracking Method 基于贝叶斯跟踪的非对比三维心脏CT图像主动脉自动检测

Medical computer vision : large data in medical imaging : third international MICCAI workshop, MCV 2013, Nagoya, Japan, September 26, 2013 : revised selected papers. MCV (Workshop) (3rd : 2013 : Nagoya-shi, Japan) Pub Date : 2013-09-26 DOI: 10.1007/978-3-319-05530-5_13

Mingna Zheng, J. Carr, Y. Ge

引用次数: 6