Learning-based topological correction for infant cortical surfaces

Shijie Hao, Gang Li, Li Wang, Yu Meng, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Reconstruction of topologically correct and accurate cortical surfaces from infant MR images is of great importance in neuroimaging mapping of early brain development. However,due to rapid growth and ongoing myelination,infant MR images exhibit extremely low tissue contrast and dynamic appearance patterns,thus leading to much more topological errors (holes and handles) in the cortical surfaces derived from tissue segmentation results,in comparison to adult MR images which typically have good tissue contrast. Existing methods for topological correction either rely on the minimal correction criteria,or ad hoc rules based on image intensity priori,thus often resulting in erroneous correction and large anatomical errors in reconstructed infant cortical surfaces. To address these issues,we propose to correct topological errors by learning information from the anatomical references,i.e.,manually corrected images. Specifically,in our method,we first locate candidate voxels of topologically defected regions by using a topology-preserving level set method. Then,by leveraging rich information of the corresponding patches from reference images,we build regionspecific dictionaries from the anatomical references and infer the correct labels of candidate voxels using sparse representation. Notably,we further integrate these two steps into an iterative framework to enable gradual correction of large topological errors,which are frequently occurred in infant images and cannot be completely corrected using one-shot sparse representation. Extensive experiments on infant cortical surfaces demonstrate that our method not only effectively corrects the topological defects,but also leads to better anatomical consistency,compared to the state-of-the-art methods.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PublisherSpringer Verlag
Pages219-227
Number of pages9
Volume9900 LNCS
ISBN (Print)9783319467191
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: 2016 Oct 212016 Oct 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9900 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
CountryGreece
CityAthens
Period16/10/2116/10/21

Fingerprint

Tissue
Neuroimaging
Sparse Representation
Voxel
Glossaries
Labels
Brain
Topology
Topological Defects
Level Set Method
Defects
Learning
Patch
Segmentation
Integrate
Experiments
Demonstrate
Experiment

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Hao, S., Li, G., Wang, L., Meng, Y., & Shen, D. (2016). Learning-based topological correction for infant cortical surfaces. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (Vol. 9900 LNCS, pp. 219-227). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46720-7_26

Learning-based topological correction for infant cortical surfaces. / Hao, Shijie; Li, Gang; Wang, Li; Meng, Yu; Shen, Dinggang.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS Springer Verlag, 2016. p. 219-227 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hao, S, Li, G, Wang, L, Meng, Y & Shen, D 2016, Learning-based topological correction for infant cortical surfaces. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. vol. 9900 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9900 LNCS, Springer Verlag, pp. 219-227, 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, Greece, 16/10/21. https://doi.org/10.1007/978-3-319-46720-7_26
Hao S, Li G, Wang L, Meng Y, Shen D. Learning-based topological correction for infant cortical surfaces. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS. Springer Verlag. 2016. p. 219-227. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-46720-7_26
Hao, Shijie ; Li, Gang ; Wang, Li ; Meng, Yu ; Shen, Dinggang. / Learning-based topological correction for infant cortical surfaces. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS Springer Verlag, 2016. pp. 219-227 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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