Fidelity-Imposed Displacement Editing for the Learn2Reg 2024 SHG-BF Challenge
This addresses the challenge of large discrepancies in multi-modal image registration for medical imaging, specifically in cancer tissue analysis, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled the problem of aligning second-harmonic generation (SHG) and bright-field (BF) microscopy images for cancer tissue analysis, proposing a multi-modal registration framework that achieved 1st place in the Learn2Reg 2024 SHG-BF Challenge.
Co-examination of second-harmonic generation (SHG) and bright-field (BF) microscopy enables the differentiation of tissue components and collagen fibers, aiding the analysis of human breast and pancreatic cancer tissues. However, large discrepancies between SHG and BF images pose challenges for current learning-based registration models in aligning SHG to BF. In this paper, we propose a novel multi-modal registration framework that employs fidelity-imposed displacement editing to address these challenges. The framework integrates batch-wise contrastive learning, feature-based pre-alignment, and instance-level optimization. Experimental results from the Learn2Reg COMULISglobe SHG-BF Challenge validate the effectiveness of our method, securing the 1st place on the online leaderboard.