Structurally Consistent MRI Colorization using Cross-modal Fusion Learning
Journal:
arXiv
Published Date:
Dec 12, 2024
Abstract
Medical image colorization can greatly enhance the interpretability of the
underlying imaging modality and provide insights into human anatomy. The
objective of medical image colorization is to transfer a diverse spectrum of
colors distributed across human anatomy from Cryosection data to source MRI
data while retaining the structures of the MRI. To achieve this, we propose a
novel architecture for structurally consistent color transfer to the source MRI
data. Our architecture fuses segmentation semantics of Cryosection images for
stable contextual colorization of various organs in MRI images. For
colorization, we neither require precise registration between MRI and
Cryosection images, nor segmentation of MRI images. Additionally, our
architecture incorporates a feature compression-and-activation mechanism to
capture organ-level global information and suppress noise, enabling the
distinction of organ-specific data in MRI scans for more accurate and realistic
organ-specific colorization. Our experiments demonstrate that our architecture
surpasses the existing methods and yields better quantitative and qualitative
results.
