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Liu, Y.* ; Müller, G.* ; Navab, N.* ; Marr, C. ; Huisken, J.* ; Peng, T.

BigFUSE: Global Context-Aware Image Fusion in Dual-View Light-Sheet Fluorescence Microscopy with Image Formation Prior.

In: (Medical Image Computing and Computer Assisted Intervention – MICCAI 2023). Berlin [u.a.]: Springer, 2023. 646-655 (Lect. Notes Comput. Sc. ; 14227 LNCS)
DOI
Light-sheet fluorescence microscopy (LSFM), a planar illumination technique that enables high-resolution imaging of samples, experiences “defocused” image quality caused by light scattering when photons propagate through thick tissues. To circumvent this issue, dual-view imaging is helpful. It allows various sections of the specimen to be scanned ideally by viewing the sample from opposing orientations. Recent image fusion approaches can then be applied to determine in-focus pixels by comparing image qualities of two views locally and thus yield spatially inconsistent focus measures due to their limited field-of-view. Here, we propose BigFUSE, a global context-aware image fuser that stabilizes image fusion in LSFM by considering the global impact of photon propagation in the specimen while determining focus-defocus based on local image qualities. Inspired by the image formation prior in dual-view LSFM, image fusion is considered as estimating a focus-defocus boundary using Bayes’ Theorem, where (i) the effect of light scattering onto focus measures is included within Likelihood; and (ii) the spatial consistency regarding focus-defocus is imposed in Prior. The expectation-maximum algorithm is then adopted to estimate the focus-defocus boundary. Competitive experimental results show that BigFUSE is the first dual-view LSFM fuser that is able to exclude structured artifacts when fusing information, highlighting its abilities of automatic image fusion.
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Publication type Article: Conference contribution
Corresponding Author
Keywords Bayesian ; Light-sheet Fluorescence Microscopy (lsfm) ; Multi-view Image Fusion; Guide
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title Medical Image Computing and Computer Assisted Intervention – MICCAI 2023
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 14227 LNCS, Issue: , Pages: 646-655 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute of AI for Health (AIH)
Grants China Scholarship Council