Multilabel, Multiscale Topological Transformation for Cerebral MRI Segmentation Post-processing

被引:0
|
作者
Tor-Diez, Carlos [1 ]
Faisan, Sylvain [2 ]
Mazo, Loic [2 ]
Bednarek, Nathalie [3 ,4 ]
Meunier, Helene [4 ]
Bloch, Isabelle [5 ]
Passat, Nicolas [3 ]
Rousseau, Francois [1 ]
机构
[1] UBL, INSERM, U1101, IMT Atlantique,LaTIM, Brest, France
[2] Univ Strasbourg, FMTS, CNRS, ICube UMR 7357, Illkirch Graffenstaden, France
[3] Univ Reims, CReSTIC, Reims, France
[4] CHU Reims, Serv Med Neonatale & Reanimat Pediat, Reims, France
[5] Univ Paris Saclay, Telecom ParisTech, LTCI, Paris, France
关键词
Homotopic deformation; Multilabel topology; Multiscale modelling; Segmentation; MRI; Brain; DIGITAL-TOPOLOGY; 3D; IMAGES; MODEL;
D O I
10.1007/978-3-030-20867-7_36
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Accurate segmentation of cerebral structures remains, after two decades of research, a complex task. In particular, obtaining satisfactory results in terms of topology, in addition to quantitative and geometrically correct properties is still an ongoing issue. In this paper, we investigate how recent advances in multilabel topology and homotopy-type preserving transformations can be involved in the development of multiscale topological modelling of brain structures, and topology-based post-processing of segmentation maps of brain MR images. In this context, a preliminary study and a proof-of-concept are presented.
引用
收藏
页码:471 / 482
页数:12
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