Combining datasets from multiple sites/scanners has been becoming increasingly more prevalent in modern neuroimaging studies. Despite numerous benefits from the growth in sample size, substantial technical variability associated with site/scanner-related effects exists which may inadvertently bias subsequent downstream analyses. Such a challenge calls for a data harmonization procedure which reduces the scanner effects and allows the scans to be combined for pooled analyses. In this work, we present MISPEL (Multi-scanner Image harmonization via Structure Preserving Embedding Learning), a multi-scanner harmonization framework. Unlike existing techniques, MISPEL does not assume a perfect coregistration across the scans, and the framework is naturally extendable to more than two scanners. Importantly, we incorporate our multi-scanner dataset where each subject is scanned on four different scanners. This unique paired dataset allows us to define and aim for an ideal harmonization (e.g., each subject with identical brain tissue volumes on all scanners). We extensively view scanner effects under varying metrics and demonstrate how MISPEL significantly improves them.
|Title of host publication||Proceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|Publication status||Published - 2021|
|Event||18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 - Virtual, Online, Canada|
Duration: 2021 Oct 11 → 2021 Oct 17
|Name||Proceedings of the IEEE International Conference on Computer Vision|
|Conference||18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021|
|Period||21/10/11 → 21/10/17|
Bibliographical noteFunding Information:
This work was supported by the following NIH/NIA grants: R01 AG063752 (D.L. Tudorascu), P30 AG10129 and UH3 NS100608 (C. DeCarli), and the University of Pittsburgh Alzheimer s Disease Research Center Grant P30 AG066468 (S. Hwang).
Acknowledgments. This work was supported by the following NIH/NIA grants: R01 AG063752 (D.L. Tudo-rascu), P30 AG10129 and UH3 NS100608 (C. DeCarli), and the University of Pittsburgh Alzheimer’s Disease Research Center Grant P30 AG066468 (S. Hwang).
© 2021 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition