Pub Date : 2020-05-13DOI: 10.1007/978-3-030-50120-4_4
W. Bastiaansen, M. Rousian, R. Steegers-Theunissen, W. Niessen, A. Koning, S. Klein
{"title":"Towards Segmentation and Spatial Alignment of the Human Embryonic Brain Using Deep Learning for Atlas-Based Registration","authors":"W. Bastiaansen, M. Rousian, R. Steegers-Theunissen, W. Niessen, A. Koning, S. Klein","doi":"10.1007/978-3-030-50120-4_4","DOIUrl":"https://doi.org/10.1007/978-3-030-50120-4_4","url":null,"abstract":"","PeriodicalId":90799,"journal":{"name":"Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- )","volume":"22 1","pages":"34 - 43"},"PeriodicalIF":0.0,"publicationDate":"2020-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86579721","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}
Pub Date : 2020-05-13DOI: 10.1007/978-3-030-50120-4_5
Sven Kuckertz, N. Papenberg, J. Honegger, T. Morgas, B. Haas, S. Heldmann
{"title":"Learning Deformable Image Registration with Structure Guidance Constraints for Adaptive Radiotherapy","authors":"Sven Kuckertz, N. Papenberg, J. Honegger, T. Morgas, B. Haas, S. Heldmann","doi":"10.1007/978-3-030-50120-4_5","DOIUrl":"https://doi.org/10.1007/978-3-030-50120-4_5","url":null,"abstract":"","PeriodicalId":90799,"journal":{"name":"Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- )","volume":"5 1","pages":"44 - 53"},"PeriodicalIF":0.0,"publicationDate":"2020-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79173035","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}
Pub Date : 2012-07-01DOI: 10.1007/978-3-642-31340-0_22
Yangming Ou, Dong Hye Ye, Kilian M Pohl, Christos Davatzikos
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].
{"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":"<p><p>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].</p>","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}
Pub Date : 2012-07-01DOI: 10.1007/978-3-642-31340-0_19
Anand A Joshi, David W Shattuck, Richard M Leahy
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.
{"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":"<p><p>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.</p>","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}
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