Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- )最新文献_第2页

Towards Segmentation and Spatial Alignment of the Human Embryonic Brain Using Deep Learning for Atlas-Based Registration 基于地图集的深度学习配准对人类胚胎大脑的分割和空间对齐

Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- ) Pub Date : 2020-05-13 DOI: 10.1007/978-3-030-50120-4_4

W. Bastiaansen, M. Rousian, R. Steegers-Theunissen, W. Niessen, A. Koning, S. Klein

引用次数: 6

Learning Deformable Image Registration with Structure Guidance Constraints for Adaptive Radiotherapy 基于结构导向约束的自适应放疗形变图像配准学习

Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- ) Pub Date : 2020-05-13 DOI: 10.1007/978-3-030-50120-4_5

Sven Kuckertz, N. Papenberg, J. Honegger, T. Morgas, B. Haas, S. Heldmann

引用次数: 6

Deep Volumetric Feature Encoding for Biomedical Images 生物医学图像的深度体积特征编码

Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- ) Pub Date : 2020-05-13 DOI: 10.1007/978-3-030-50120-4_9

B. Avants, E. Greenblatt, J. Hesterman, N. Tustison

引用次数: 1

An Image Registration Framework for Discontinuous Mappings Along Cracks 一种沿裂纹不连续映射的图像配准框架

Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- ) Pub Date : 2020-05-13 DOI: 10.1007/978-3-030-50120-4_16

H. Aggrawal, M. S. Andersen, J. Modersitzki

引用次数: 3

Multi-channel Image Registration of Cardiac MR Using Supervised Feature Learning with Convolutional Encoder-Decoder Network 基于监督特征学习和卷积编码器-解码器网络的心脏MR多通道图像配准

Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- ) Pub Date : 2020-05-13 DOI: 10.1007/978-3-030-50120-4_10

Xuesong Lu, Y. Qiao

引用次数: 0

Learning-Based Affine Registration of Histological Images 基于学习的组织图像仿射配准

Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- ) Pub Date : 2020-05-13 DOI: 10.1007/978-3-030-50120-4_2

Marek Wodzinski, H. Müller

引用次数: 8

Enabling Manual Intervention for Otherwise Automated Registration of Large Image Series 启用手动干预，否则自动注册大图像系列

Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- ) Pub Date : 2020-05-13 DOI: 10.1007/978-3-030-50120-4_3

R. Grothausmann, Dženan Zukić, Matt McCormick, C. Mühlfeld, L. Knudsen

引用次数: 1

Biomedical Image Registration: 9th International Workshop, WBIR 2020, Portorož, Slovenia, December 1–2, 2020, Proceedings 生物医学图像配准:第九届国际研讨会，WBIR 2020, portororov，斯洛文尼亚，12月1-2日，2020,Proceedings

Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- ) Pub Date : 2020-01-01 DOI: 10.1007/978-3-030-50120-4

J. McClelland, J. Kybic, O. Goksel, E. Bertino

引用次数: 0

Validation of DRAMMS among 12 Popular Methods in Cross-Subject Cardiac MRI Registration. 12种常用心脏MRI配准方法的DRAMMS验证。

Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- ) Pub Date : 2012-07-01 DOI: 10.1007/978-3-642-31340-0_22

Yangming Ou, Dong Hye Ye, Kilian M Pohl, Christos Davatzikos

{"title":"Validation of DRAMMS among 12 Popular Methods in Cross-Subject Cardiac MRI Registration.","authors":"Yangming Ou, Dong Hye Ye, Kilian M Pohl, Christos Davatzikos","doi":"10.1007/978-3-642-31340-0_22","DOIUrl":"https://doi.org/10.1007/978-3-642-31340-0_22","url":null,"abstract":"Cross-subject image registration is the building block for many cardiac studies. In the literature, it is often handled by voxel-wise registration methods. However, studies are lacking to show which methods are more accurate and stable in this context. Aiming at answering this question, this paper evaluates 12 popular registration methods and validates a recently developed method DRAMMS [16] in the context of cross-subject cardiac registration. Our dataset consists of short-axis end-diastole cardiac MR images from 24 subjects, in which non-cardiac structures are removed. Each registration method was applied to all 552 image pairs. Registration accuracy is approximated by Jaccard overlap between deformed expert annotation of source image and the corresponding expert annotation of target image. This accuracy surrogate is further correlated with deformation aggressiveness, which is reflected by minimum, maximum and range of Jacobian determinants. Our study shows that DRAMMS [16] scores high in accuracy and well balances accuracy and aggressiveness in this dataset, followed by ANTs [13], MI-FFD [14], Demons [15], and ART [12]. Our findings in cross-subject cardiac registrations echo those findings in brain image registrations [7].","PeriodicalId":90799,"journal":{"name":"Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- )","volume":"7359 ","pages":"209-219"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5462118/pdf/nihms861246.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35080509","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: 24

A Method for Automated Cortical Surface Registration and Labeling. 一种皮质表面自动配准与标记方法。

Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- ) Pub Date : 2012-07-01 DOI: 10.1007/978-3-642-31340-0_19

Anand A Joshi, David W Shattuck, Richard M Leahy

{"title":"A Method for Automated Cortical Surface Registration and Labeling.","authors":"Anand A Joshi, David W Shattuck, Richard M Leahy","doi":"10.1007/978-3-642-31340-0_19","DOIUrl":"https://doi.org/10.1007/978-3-642-31340-0_19","url":null,"abstract":"Registration and delineation of anatomical features in MRI of the human brain play an important role in the investigation of brain development and disease. Accurate, automatic and computationally efficient cortical surface registration and delineation of surface-based landmarks, including regions of interest (ROIs) and sulcal curves (sulci), remain challenging problems due to substantial variation in the shapes of these features across populations. We present a method that performs a fast and accurate registration, labeling and sulcal delineation of brain images. The new method presented in this paper uses a multiresolution, curvature based approach to perform a registration of a subject brain surface model to a delineated atlas surface model; the atlas ROIs and sulcal curves are then mapped to the subject brain surface. A geodesic curvature flow on the cortical surface is then used to refine the locations of the sulcal curves sulci and label boundaries further, such that they follow the true sulcal fundi more closely. The flow is formulated using a level set based method on the cortical surface, which represents the curves as zero level sets. We also incorporate a curvature based weighting that drives the curves to the bottoms of the sulcal valleys in the cortical folds. Finally, we validate our new approach by comparing sets of automatically delineated sulcal curves it produced to corresponding sets of manually delineated sulcal curves. Our results indicate that the proposed method is able to find these landmarks accurately.","PeriodicalId":90799,"journal":{"name":"Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- )","volume":"7359 ","pages":"180-189"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-642-31340-0_19","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33869977","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: 52