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Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation
- Source :
- Medical Image Analysis, Dubost, F, Bruijne, M D, Nardin, M, Dalca, A V, Donahue, K L, Giese, A-K, Etherton, M R, Wu, O, Groot, M D, Niessen, W, Vernooij, M, Rost, N S & Schirmer, M D 2020, ' Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation ', Medical Image Analysis, vol. 63, 101698 . https://doi.org/10.1016/j.media.2020.101698, Medical image analysis 63, 101698 (2020). doi:10.1016/j.media.2020.101698, Med Image Anal, Medical Image Analysis, 63:Unsp 101698. Elsevier
- Publication Year :
- 2020
-
Abstract
- Registration is a core component of many imaging pipelines. In case of clinical scans, with lower resolution and sometimes substantial motion artifacts, registration can produce poor results. Visual assessment of registration quality in large clinical datasets is inefficient. In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain. The method consists of automatically segmenting the ventricles of a given scan using a neural network, and comparing the segmentation to the atlas ventricles propagated to image space. We used the proposed method to improve clinical image registration to a general atlas by computing multiple registrations - one directly to the general atlas and others via different age-specific atlases - and then selecting the registration that yielded the highest ventricle overlap. Finally, as an example application of the complete pipeline, a voxelwise map of white matter hyperintensity burden was computed using only the scans with registration quality above a predefined threshold. Methods were evaluated in a single-site dataset of more than 1000 scans, as well as a multi-center dataset comprising 142 clinical scans from 12 sites. The automated ventricle segmentation reached a Dice coefficient with manual annotations of 0.89 in the single-site dataset, and 0.83 in the multi-center dataset. Registration via age-specific atlases could improve ventricle overlap compared to a direct registration to the general atlas (Dice similarity coefficient increase up to 0.15). Experiments also showed that selecting scans with the registration quality assessment method could improve the quality of average maps of white matter hyperintensity burden, instead of using all scans for the computation of the white matter hyperintensity map. In this work, we demonstrated the utility of an automated tool for assessing image registration quality in clinical scans. This image quality assessment step could ultimately assist in the translation of automated neuroimaging pipelines to the clinic.
- Subjects :
- FOS: Computer and information sciences
Similarity (geometry)
Computer science
Image quality
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Image registration
Health Informatics
Article
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Neuroimaging
Sørensen–Dice coefficient
FOS: Electrical engineering, electronic engineering, information engineering
Image Processing, Computer-Assisted
Humans
Radiology, Nuclear Medicine and imaging
Segmentation
Computer vision
ddc:610
diagnostic imaging [Brain]
Radiological and Ultrasound Technology
business.industry
Atlas (topology)
Deep learning
Image and Video Processing (eess.IV)
Brain
Electrical Engineering and Systems Science - Image and Video Processing
Magnetic Resonance Imaging
Computer Graphics and Computer-Aided Design
Neural Networks, Computer
Computer Vision and Pattern Recognition
Artificial intelligence
Artifacts
business
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- ISSN :
- 13618415
- Volume :
- 63
- Database :
- OpenAIRE
- Journal :
- Medical Image Analysis
- Accession number :
- edsair.doi.dedup.....bc1491d3d6f0d39e3392334008adfde7