English

Structurally Consistent MRI Colorization using Cross-modal Fusion Learning

Image and Video Processing 2024-12-17 v1 Artificial Intelligence Computer Vision and Pattern Recognition

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.

Keywords

Cite

@article{arxiv.2412.10452,
  title  = {Structurally Consistent MRI Colorization using Cross-modal Fusion Learning},
  author = {Mayuri Mathur and Anav Chaudhary and Saurabh Kumar Gupta and Ojaswa Sharma},
  journal= {arXiv preprint arXiv:2412.10452},
  year   = {2024}
}

Comments

9 pages, 6 figures, 2 Tables

R2 v1 2026-06-28T20:34:38.379Z